Science topic

Metaheuristic Algorithm - Science topic

Explore the latest questions and answers in Metaheuristic Algorithm, and find Metaheuristic Algorithm experts.
Questions related to Metaheuristic Algorithm
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Over the last few decades, there have been numerous metaheuristic optimization algorithms developed with varying inspiration sources. However, most of these metaheuristics have one or more weaknesses that affect their performances, for example:
  1. Trapped in a local optimum and are not able to escape.
  2. No trade-off between the exploration and exploitation potentials
  3. Poor exploitation.
  4. Poor exploration.
  5. Premature convergence.
  6. Slow convergence rate
  7. Computationally demanding
  8. Highly sensitive to the choice of control parameters
Metaheuristics are frequently improved by adding efficient mechanisms aimed at increasing their performance like opposition-based learning, chaotic function, etc. What are the best efficient mechanisms you suggest?
Relevant answer
Answer
Fact: A metaheuristic neighborhood search that is trapped in a local optimum for a rugged-elementary landscape can be modified to a 2-step neighborhood search (by using the squared neighborhood adjacency matrix). Next paper shows that this leads to a favorable smooth-elementary landscape.
  • asked a question related to Metaheuristic Algorithm
Question
2 answers
I am not able to find a suitable answer in any research paper, why exacly we use metaheuristic algorithms with MCDM techniques to solve decision making problems. Though only MCDM techniques are enough or metaheuristic itself is enough sometiome to solve the same problem.
Relevant answer
Answer
As GA and PSO etc. use a population of candidate solutions to search the objective space in parallel, it is possible to develop an approximation of the Pareto front of the MCDM problem in a single optimisation run (i.e. look at the mulit-objective and many-objective variants of the GA such as NSGA-II etc). Knowing the potential shape of the Pareto front can be a massive help in being able to use more classical MCDM methods to target specific regions of the Pareto front that are of interest. It very much depends on the nature of the optimisation problem being addressed as to how effective GA/PSO etc. are at providing high-quality solutions to the problem from a single run, but using the methods as a first-step in understanding the properties of the problem, followed up with more traditional methods, has for me always resulted in far superior final solutions than when GA/PSO or classical MCDM are used on their own.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Why use metaheuristic algorithms when there are so many mathematical optimization tools available, like GAMS?
Relevant answer
Answer
Ankur Maheshwari I agree with Ben Cardoen that a heuristic/partial search algorithm may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity where commercial solvers may struggle. But the key issue of heuristics is that they do not guarantee that a globally optimal solution can be found on some class of problems when compared with commercial solvers such as Ipopt, Cplex, Mosek, Gurobi,..., for NLP or LP or MILP or MINLP problems.
  • asked a question related to Metaheuristic Algorithm
Question
2 answers
I want to use pyomo to model MILP problems in python such as Green Vehicle Routing Problem and others but use metaheuristics algorithms (GA, Harmony search, etc.) to solve those problems instead of using pyomo solvers, its part of my phd project and can not use derivative-based solvers at all; If the answer that I can not, what is the alternative to model the problems in python then solve them with metaheuristics algorithms because i found pyomo easy and time saving to be used in formulating the problems.
  • asked a question related to Metaheuristic Algorithm
Question
11 answers
In general, the optimized model gives more accurate results than the local model but in my case, the traditional regression model shows a higher R2 value than the optimized model? Thank you in advance for your kind feedback.
Relevant answer
Answer
GridSearchCV is a technique to search through the best parameter values from the given set of the grid of parameters. It is basically a cross-validation method. the model and the parameters are required to be fed in. Best parameter values are extracted and then the predictions are made.
  • asked a question related to Metaheuristic Algorithm
Question
7 answers
In my case I find PSO and GA works good
Relevant answer
Answer
Hi Khairul, you might want to look at Prof. Alireza Soroudi's work. He covers many interesting topics around this subject. Good luck!
  • asked a question related to Metaheuristic Algorithm
  • asked a question related to Metaheuristic Algorithm
Question
2 answers
I am interested in the use of Extreme Value Theory (EVT) to estimate global optima of optimization problems (using heuristic and metaheuristic algorithms), however, it is a bit difficult to find them since the use of EVT is not usually the main objective of the studies. Could you help me by sharing articles where this procedure is used? Thank you in advance.
Relevant answer
Answer
Bettinger, P., J. Sessions, and K. Boston. 2009. A review of the status and use of validation procedures for heuristics used in forest planning. Mathematical and Computational Forestry & Natural-Resource Sciences. 1(1): 26-37.
Bettinger, P., J. Sessions, and K.N. Johnson. 1998. Ensuring the compatibility of aquatic habitat and commodity production goals in eastern Oregon with a Tabu search procedure. Forest Science. 44(1): 96-112.
Boston, K. and P. Bettinger. 1999. An analysis of Monte Carlo integer programming, simulated annealing, and tabu search heuristics for solving spatial harvest scheduling problems. Forest Science. 45(2): 292-301.
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
I have simulated an industrial process by ASPEN and I want to optimize the operational parameters (decision) to maxmize the final yield (objective) while minmizing the energy comsumption (second objective)
Therefore, I would like to build a ML model to use it to optimize/decide the best operational parameters.
My plan is to use metaheuristic models such GA (Genetic Algorithm) but I have a difficulty to know what are the steps to build the model? How this optimization algorithm can be be implimented as a supervised ML Model?
Relevant answer
Answer
Dear MO,
Generally, machine learning methods have an algorithm to learn and generalise from recorded data to construct projections on new samples, and they do not use for optimising a design directly. There are a large number of optimisation methods that can be applied to your problem, but you need a fitness (objective) function in order to evaluate the new solution proposed by the optimisation method. You can easily apply an optimisation method if you have such a function.
Please let me know if you need more information in this way.
  • asked a question related to Metaheuristic Algorithm
Question
16 answers
Dear Researchers,
I'm looking for the implementation of advanced metaheuristic algorithms in Python. I would be incredibly thankful if someone could assist me with the execution of evolutionary algorithms or can provide me with the necessary codes in Python.
Thank you very much.
Relevant answer
Answer
Dear Muhammad,
In addition to DEAP which is a great one, I suggest another library : PyMOO which also provides complementary single & multi objectives solvers for discrete and continuous variables problems.
Best regards !
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
I am a beginner at the optimization of trusses with metaheuristic algorithms. anyone, please help me get any MatLab code that helps me optimize 10 bar truss?
Relevant answer
Answer
Do you have a mathematical model of your problem? Have you implemented it?
I would run a classical optimization solver before resorting to a metaheuristic, though.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
What are the most effective operators that can be incorporated into metaheuristic algorithms to rectify the problem of low population diversity and hence premature convergence? Any additional tips on this matter would be very helpful.
Relevant answer
Answer
I recommend that you use the tangent flight
X=X+tan(rand*pi) % large flight
or
X=X+ sign(rand-0.5)*tan(rand*pi/2.5) % small flight
Layeb, Abdesslem. "Tangent search algorithm for solving optimization problems." Neural Computing and Applications (2022): 1-32.
  • asked a question related to Metaheuristic Algorithm
Question
19 answers
Suppose that if we compare two metaheuristics X and Y in a given real problem, X returns a better solution than Y, while when we use the same metaheuristics to solve global optimization problems, Y returns a better solution than X. Does this make sense? what is the reason?
Relevant answer
Answer
This is a normal occurrence in all mathematical problems. There is no perfect solution that solves all problems, especially nonlinear problems. Always strive to improve and work harder. Thank you very much.
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
Is there any libraries of keras python or software where we can use different metaheuristic algorithms to train Artificial neural network ? or Books that explain this methodology
By softwares, i mean Rapidminer, Matlab, Knime, Microsoft Azure ML ... etc or libraries of python, C++ that enable to use different metaheuristic algorithms to train ANN and SVM models.
The goal is to compare a developped model to these different combinations of metaheuristic trained models.
Relevant answer
Answer
The most popular Python machine learning library for developing machine learning algorithms is Scikit-learn. It was built on NumPy and SciPy, two Python libraries. Scikit-learn is a Python toolkit that provides a consistent interface for supervised and unsupervised learning algorithms.
Keras is a lightweight Python deep learning package that may be used with Theano or TensorFlow. It was created to facilitate implementing deep learning models for research and development as quickly and simply as feasible.
NeuroLab is a Python Neural Network Library that is both simple and powerful. This library includes neural network-based networks, train algorithms, and a flexible framework for creating and exploring new networks.
  • asked a question related to Metaheuristic Algorithm
Question
16 answers
Like other meta-heuristic algorithms, some algorithms tend to be trapped in low diversity, local optima and unbalanced exploitation ability.
1- Enhance its exploratory and exploitative performance.
2- Overcome premature convergence (increase the fast convergence) and ease of falling (trapped) into a local optimum.
3- Increase the diversity of population and alleviate the prematurity convergence problem
4- The algorithm suffers from an immature balance between exploitation and exploration.
5- Maintain the diversity of solutions during the search, so that the tendency of stagnation towards the sub-optimal solutions can be avoided and the convergence rate can be boosted to obtain more accurate optimal solutions.
6- Slow convergence speed, inability to jump out of local optima and fixed step length.
7- Improve its population diversity in the search space.
Relevant answer
Answer
like Mr . Joel Chacón, I think also that question is a dependent problem, there is no exact answer.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
I am looking a free of charge International Conference in metaheuristic algorithm or data mining issue, is there ay one can help me?
Relevant answer
Sikirat Aina thanks.
  • asked a question related to Metaheuristic Algorithm
Question
10 answers
Both genetic algorithms and metaheuristic algorithms are optimization algorithms. Is one category of these two is included under the other?
Relevant answer
Answer
Evolutionary algorithms, physics-based algorithms, swarm-based algorithms, and human-based algorithms are the four primary types of meta-heuristic algorithms. These algorithms are based on human or animal behavior, as well as some physical behaviors of molecules, and so forth.
Genetic algorithms, partial swarm optimization, grey wolff optimization, ant colony optimization, and Social-Based Algorithm are some example.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
I have a multi-objective optimization with the following properties:
Objective function: thee minimization objective functions) two non-linear functions and one linear function
Decision variable: two real variables (Bounded)
Constraint: three linear constraint (two bounding constraint and one relationship constraint)
Problem type: non-convex
Solution required: Global optimum
I have used two heuristic algorithms to solve the problem NSGA-II and NSGA-III.
I have performed NSGA-II and NSGA-III for the following instances (population size, number of generations, maximum number of functional evaluations(i.e. pop size x no. of gen)): (100,10,1000), (100,50,5000),(100,100,10000), (500, 10, 1000), (500, 50, 25000), and (500,100,50000).
My observations:
Hypervolume increases with increase in number of functional evaluations. However, for a given population size, as the number of generation increases the hypervolume reduces. Which I think should rather increase. Why am I getting such an answer?
Relevant answer
Answer
Greetings to you all.
Please how can I find MATLAB code for Accelerated Particle Swarm Optimization algorithm for tuning PID controller.
  • asked a question related to Metaheuristic Algorithm
Question
49 answers
Can anyone suggest a recent metaheuristic algorithm(s)? Especially those developed between 2020 and to date.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
I want to use Firefly algorithm for feature selection. Can anyone help in suggesting different transformation methods?
Relevant answer
Answer
Hi,
Totally, there are two ways to convert continuous metaheuristic algorithms to binary form. First, you can use logical operators, and the second is using transfer functions like S-shaped, V-shaped, etc. Transfer functions (TF) are the easiest ways. You can study the following papers for more information:
  • asked a question related to Metaheuristic Algorithm
Question
40 answers
Hi everyone,
We have implemented four metaheuristic algorithms to solve an optimization problem. Each algorithm is repeated 30 times for an instance of the problem, and we have stored the best objective function values for 30 independent runs for each algorithm.
We want to compare these four algorithms. Apart from maximum, minimum, average, and standard deviation, is there any statistical measure for comparison?
Alternatively, we have four independent samples each of size 30, and we want to test the null hypothesis that the means (or, medians) of these four samples are equal against an alternative hypothesis that they are not. What kind of statistical test should we perform?
Regards,
Soumen Atta
Relevant answer
Answer
1-Find the average of fitness functions (30 runs) for two algorithms separately: find the p value using t-test.
2-Find the average of fitness functions (30 runs) for more than 2 algorithms separately: find the p value using ANOVA.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Hi
I intend to define a frequency domain objective function to design an optimal controller for Load-Frequency Control (LFC) of a power system. My purpose is to optimize this objective function (finding the proper location of zeros and poles) using meta-heuristic methods.
I will be happy if you share your valuable relevant and informative experiences, references and articles in this field including how to define and how to code.
Thanks
  • asked a question related to Metaheuristic Algorithm
Question
31 answers
Dear friends,
We invite all researchers and practitioners who are developing algorithms, systems, and applications, to share their results, ideas, and experiences.
Topics of interest include, but are not limited to, the following:
Hybrid Metaheuristics
Theoretical aspects of hybridization
Automated parameter tuning
Parallelization
Evolutionary Computation Algorithms
Swarm Optimization
Multi-objective optimization
Multilevel segmentation
Object recognition
Computer vision
Image processing
Filtering and enhancement
Morphology
Edge detection and segmentation
Feature extraction
Quantum Image Processing
Image thresholding
Applications
Relevant answer
Answer
Prof. Diego Oliva: Yes, with pleasure. Thank you for this useful information.
  • asked a question related to Metaheuristic Algorithm
Question
7 answers
I'm working on some optimal strategies for an environmental surveillance network. My solution is almost based on the meta-heuristic solution. I have to know what the advantages or disadvantages are of heuristic and meta-heuristic optimizations.
Relevant answer
  • asked a question related to Metaheuristic Algorithm
Question
14 answers
Hello scientific community
Do you noting the following:
[I note that when a new algorithms has been proposed, most of the researchers walk quickly to improve it and apply for solving the same and other problems. I ask now, so why the original algorithm if it suffer from weakness, why the need for a new algorithm if there are an existing one that solved the same problems, I understand if the new algorithm solved the unsolved problem so welcome, else why?]
Therefore, I ask, is the scientific community need a novel metaheuristic algorithms (MHs) rather than the existing.
I think, we need to organized the existing metaheuristic algorithms and mentioned the pros and cons for each one, the solved problems by each one.
The repeated algorithms must be disappear and the complex also.
The dependent algorithms must be disappeared.
We need to benchmark the MHs similar as the benchmark test suite.
Also, we need to determine the unsolved problems and if you would like to propose a novel algorithm so try to solve the unsolved problem else stop please.
Thanks and I wait for the reputable discussion
Relevant answer
Answer
The last few decades have seen the introduction of a large number of "novel" metaheuristics inspired by different natural and social phenomena. While metaphors have been useful inspirations, I believe this development has taken the field a step backwards, rather than forwards. When the metaphors are stripped away, are these algorithms different in their behaviour? Instead of more new methods, we need more critical evaluation of established methods to reveal their underlying mechanics.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
I am conducting a study on how to best use hybrid methods to improve MPPT accuracy and speed. I've hit a stumbling block with the experiment design. I've been advised to use a factorial design but I'm not sure which factors to use as independent variables. My research (honours research) is based on using the grey wolf optimization and p&o methods in an mppt. I have chosen the perturbation step size and number of iterations as independent variable and took the convergence time and accuracy as dependent variables. I've never done a factorial experiment before, are there any suggestions as to which independent variables I should pick that would be worth studying?
Relevant answer
Answer
I have not done any formal factorial experiments in my work but I think the these two wiki articles are good starting points.
Factorial experiment - I think this is a pretty good article. Not too long, has an example and references look legit.
Factor analysis - might be more info than you need but worth a quick look
  • asked a question related to Metaheuristic Algorithm
Question
13 answers
Hi,
I know that PSO is an iterative and evolutionary metaheuristic algorithm but, to save my energy, I have to get some clues about coding it in GAMS.
Is it possible to implemnt it into GAMS or no need to struggle with it?
Regards,
Relevant answer
Answer
I would say don't bother with GAMS if you want to solve your problem using PSO or other metaheuristics. Use MATLAB, C++, Python or something and just code your algorithm. That gives you more flexibility. If your problem is possible to solve with one of the solvers in GAMS, then there is no need for PSO. But otherwise, I suggest writing your own code. You can find some examples on the internet, to make things easier.
  • asked a question related to Metaheuristic Algorithm
Question
18 answers
Hi, everyone
Could someone recommend papers about
Particle swarm optimization (PSO technique), using MATLAB, please?
Thanks in advance
Relevant answer
Answer
I believe you're primarily looking for material that would help someone understand and get started using the technique. I would recommend learning about the algorithm the same way I did. I first watched a couple of YouTube videos explaining the theoretical aspect of the algorithm. This video is the best in my opinion: https://www.youtube.com/watch?v=JhgDMAm-imI
After you get the gist of the algorithm's mechanism, go ahead and watch the MATLAB coding process in another 3 part video series provided by yarpiz: https://www.youtube.com/watch?v=sB1n9a9yxJk (I would highly recommend visiting the site if you're set on entering the world of metaheuristic optimization. The site is loaded with matlab implementations of many well-known nature inspired algorithms that one can work with in his projects or research.)
Finally, to cement everything together and for a more in-depth reading about the algorithm, I recommend reading the book"Particle Swarm Optimization by Maurice Clerc." Good luck.
  • asked a question related to Metaheuristic Algorithm
Question
16 answers
The routing problem can be easily solved using ILP or mixed ILP, why metaheuristic algorithms are required to solve this problem?
Relevant answer
Answer
Some useful contextualization for the simplest variant of the vehicle routing problem (the CVRP):
• The Christofides, Mingozzi, and Toth [CMT] vehicle routing instances with up to 200 clients have been released in 1979, and it took around 35 years of research and progress on exact methods to solve them optimally. In particular, simple MILP formulation for the CVRP (e.g., two-index vehicle flow formulation) can only solve some problems with a few dozes of clients.
• For the newer Uchoa et al. [X] instances with 100 to 1000 clients, there are monetary prizes for anyone able to consistently solve them to optimality (http://vrp.atd-lab.inf.puc-rio.br/index.php/en/cvrp-challenge). So far, this prize remains unclaimed, and many instances with as few as 300 customers remain unsolved.
• Practical applications often involve >5,000 clients (!!!) and *many* complicating constraints, additional decisions, and objectives called "attributes". Application cases with over 50,000 stops/clients are also quite common in courier delivery and refuse collection. Opting for an optimal solution through MILP in these situations is a guaranteed failure, and it usually reflects a lack of practical experience in the field. Even good lower bounds can be hard to get when problem size grows.
  • asked a question related to Metaheuristic Algorithm
Question
17 answers
Hi, I am working on a research paper in which I want to compare the performance of several (meta)heuristics (including GA) in solving a certain problem. I have run each algorithm several times and found out that my GA is not able to find the good solution that other (meta)heuristics find in a short time. It converges to a solution which I know is not the best (because other algorithms converge to a way better solution. I have increased the mutation rate to 0.2 in order to avoid getting trapped in a local optima and my crossover rate is 0.9.
I want to have an acceptable comparison/evaluation of the performance of these algorithms, So
my question is: Is there a problem with my GA or can I simply report the GA solution and explain that it performs poorly?
Relevant answer
Answer
One possible explanation is that standard GAs do not include local search as part of their implementation. Metaheuristics such as Iterated Local Search incorporate local search and that makes them very effective. One solution is to hybridize the GA with local search, which is sometimes called Memetic Algorithms.
Another explanation is that sometimes Crossover operators are not very effective. So a GA with a very effective crossover can have good performance, but another GA with a mediocre crossover operator can have poor performance.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
List the critical solved and unsolved global/constrained/complex optimization problems, combinatorial and engineering problems?
Metaheuristic algorithms can be classified in many ways
Several Metaheuristic algorithms are proposed to solve different optimization problems, but the solved is resolved and the unsolved is still unsolved.
List the unsolved problem or the critical solved problem that need an optimal solution to help the researcher to solve the unsolved problems.
Thanks for you contributions
Relevant answer
Answer
Every year math programmers devise optimal solutions for more and more complex and larger problems, while metaheuristics enthusiasts still haven't caught up with the times. I find it puzzling - like it was a virtue to not finding an optimal solution. For several of us, it is quite puzzling, actually. I would be interested to hear why optimal solutions are not useful, or interesting.
  • asked a question related to Metaheuristic Algorithm
Question
24 answers
Mathematical programming is the best optimization tool with many years of strong theoretical background. Also, it is demonstrated that it can solve complex optimization problems on the scale of one million design variables, efficiently. Also, the methods are so reliable! Besides, there is mathematical proof for the existence of the solution and the globality of the optimum.
However, in some cases in which there are discontinuities in the objective function, there would be some problems due to non-differentiable problem. Some methods such as sub-gradients are proposed to solve such problems. However, I cannot find many papers in the state-of-the-art of engineering optimization of discontinuous optimization using mathematical programming. Engineers mostly use metaheuristics for such cases.
Can all problems with discontinuities be solved with mathematical programming? Is it easy to implement sub-gradients for large scale industrial problems? Do they work in non-convex problems?
A simple simple example of such a function is attached here.
Relevant answer
Answer
Your ideas for dividing the region and using local optimizer are so nice!
Thanks a lot!
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Instead of manual tuning of algorithm's parameters, it is recommended to utilize automatic algorithm configuration software. Mostly beacuse it was shown that they increase manyfold the algorithm's perfomance. However, there are some differences among the proposed configuration software and beside those listed in (Eiben, Smit, 2011) it is important to gather experiances from the researchers. I would like to hear how does one decide on the stopping criteria, or values for parameters, for heuristic steps within the stochastic algorithm... there are so many questions.
Relevant answer
Answer
As you mentioned, parameter tuning studies for a metaheuristic is quite important. Researchers should determine proper control parameters for their optimization problem to increase the success of the algorithm. However, many researchers uses algorithm parameters suggested by their developers as this is can be a time consuming task via a trial and error approach. Also, I agree that self-adaptive versions of these algorithms can increase both effectiveness and performance compared to their original versions. However, they can require definition of extra parameters as well in the algorithm. In my cases, I prefer to use original versions of the algorithms via a parameter tuning study. Besides, I use two termination criteria including a predefined maksimun generation number and a tolerans value. If the algorithm provides a misfit value less than the tolerans, it stops before the reaching maksimum number of generation. Sometimes I take into account a number of successive generations. For instance, if the solution do not improve during the last 30 generations, I stop the algorithm. This provides relatively decrease the high computation cost due to much execution of the forward equation. This is the biggest drawback of the global optimization compared to derivative-based approaches considering high-dimensional optimization problems.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
Dear colleagues
I have experimental tests databases for steel structures (bracing and outrigger). If any of you have interest and specialty in this area can join our research group. We decided to include artificial neural network (ANN) combined with metaheuristic algorithm to estimate the results of experimental test.
I can share more information if you are interested.
You may contact me here or my personal email address:
Relevant answer
Answer
I am working on NN and metaheuristcs
  • asked a question related to Metaheuristic Algorithm
Question
6 answers
When any metaheuristic algorithm is applied then how we can say that the problem is free from premature or local convergence.
Relevant answer
Answer
Try using a different optimizer, like Adam-it typically works really well
  • asked a question related to Metaheuristic Algorithm
Question
32 answers
what is the Best Metaheuristic Algorithm for solving NP-Hard Problem (Ex. TSP)
Relevant answer
Answer
thanks
  • asked a question related to Metaheuristic Algorithm
Question
18 answers
Is it equal to the size of the population given initially?
E.g. If I taken initial population size=100 and no. of generations=100, then is it necessary that the size of population in 100th generation is 100? 
I have taken Population size =100
No.of generations=100 (default)
stall generations=50
No.of variables=28
after i run GA in optim tool, I have observed that population size that is obtained in last generation is 100x28
Please explain.
Relevant answer
Answer
hello please how i can solve my problem with the AG method if i have a variable size population
  • asked a question related to Metaheuristic Algorithm
Question
23 answers
Bat-inspired algorithm is a metaheuristic optimization algorithm developed by Xin-She Yang in 2010. This bat algorithm is based on the echolocation behaviour of microbats with varying pulse rates of emission and loudness.
The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity vi at position (solution) xi with a varying frequency or wavelength and loudness Ai. As it searches and finds its prey, it changes frequency, loudness and pulse emission rate r. Search is intensified by a local random walk. Selection of the best continues until certain stop criteria are met. This essentially uses a frequency-tuning technique to control the dynamic behaviour of a swarm of bats, and the balance between exploration and exploitation can be controlled by tuning algorithm-dependent parameters in bat algorithm. (Wikipedia)
What are the applications of bat algorithm? Any good optimization papers using bat algorithm? Your views are welcome! - Sundar
Relevant answer
Answer
Bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010 and BA has been found to be very efficient .
  • asked a question related to Metaheuristic Algorithm
Question
10 answers
I have applied metaheuristic algorithms such as PSO, GA in my research field which is recommender system but what I have found is these algorithms are very time consuming and really not practical, though the result is better than the existing algorithms. In recommender systems, we need fast algorithm. Thank you.
Relevant answer
Answer
Of course not !!! Metaheuristics are here to solve the problem of the time.
Metaheuristics are cleverly done since the exhaustive search with the infinite running time is useless especially for large-scale and real-life optimization problems.
Table one of the following reference gives a detailed answer to this issue:
  • asked a question related to Metaheuristic Algorithm
Question
25 answers
Some metaheuristics prove their superior performance in some kind of problems. Some of them are continuous optimization problems and others in discrete or binary optimization problems.
Relevant answer
Answer
Simply look at this research:
N. K. T. El-Omari, "Sea Lion Optimization Algorithm for Solving the Maximum Flow Problem", International Journal of Computer Science and Network Security (IJCSNS), e-ISSN: 1738-7906, DOI: 10.22937/IJCSNS.2020.20.08.5, 20(8):30-68, 2020.
It has a complete discussion about your question.
Or refer to the same paper at the following address:
  • asked a question related to Metaheuristic Algorithm
Question
22 answers
Since the early 90’s, metaheuristic algorithms have been continually improved in order to solve a wider class of optimization problems. To do so, different techniques such as hybridized algorithms have been introduced in the literature. I would be appreciate if someone can help me to find some of the most important techniques used in these algorithms.
- Hybridization
- Orthogonal learning
- Algorithms with dynamic population
Relevant answer
Answer
The following current-state-of-the-art paper has the answer for this question:
N. K. T. El-Omari, "Sea Lion Optimization Algorithm for Solving the Maximum Flow Problem", International Journal of Computer Science and Network Security (IJCSNS), e-ISSN: 1738-7906, DOI: 10.22937/IJCSNS.2020.20.08.5, 20(8):30-68, 2020.
Or simply refer to the same paper at the following address:
  • asked a question related to Metaheuristic Algorithm
Question
10 answers
Hello
I have a question if someone can answer me please..
are there other ways to compute the performance of a metaheuristic algorithm if there is no best known solution to compare with(I created my own data set of instances). I know that it is possible to do a comparison with lower bounds, but I want to know if other manners exist. I'm talking about a mono objective problem. The metaheristic I used is one of the variants of VNS.
Thank you in advance
Relevant answer
Answer
Dear everybody,
More info can be found here:
N. K. T. El-Omari, "Sea Lion Optimization Algorithm for Solving the Maximum Flow Problem", International Journal of Computer Science and Network Security (IJCSNS), e-ISSN: 1738-7906, DOI: 10.22937/IJCSNS.2020.20.08.5, 20(8):30-68, 2020.
Or simply refer to the same paper at the following address:
  • asked a question related to Metaheuristic Algorithm
Question
36 answers
Recently, there have been published many metaheuristic algorithms mostly based on swarm intelligence. The good future for these field can be applying these algorithms for solving some real problems in the different sectors such as business, marketing, management, intelligent traffic systems, engineering, health care and medicine. Please let's discuses about their applications in the real world and share our case studies.
Relevant answer
Answer
More info can be found here:
N. K. T. El-Omari, "Sea Lion Optimization Algorithm for Solving the Maximum Flow Problem", International Journal of Computer Science and Network Security (IJCSNS), e-ISSN: 1738-7906, DOI: 10.22937/IJCSNS.2020.20.08.5, 20(8):30-68, 2020.
Or simply refer to the same paper at the following address:
  • asked a question related to Metaheuristic Algorithm
Question
16 answers
When two metaheuristic algorithms are combined and a hybrid metaheuristic is developed, how can we evaluate the hybrid metaheuristic in order to make sure that it is better than the two original ones? Is it possible to do that with a small number of iterations (<3000)? Should we test the algorithms with the same number of populations (feasible solutions)?
Relevant answer
Answer
Dear Shervin,
You should evaluate the new hybrid algorithm with a similar termination criterion and computational time as the baseline algorithms. I generally recommend to measure performance (average and best solution quality over several runs with different seeds) and (average) computational time on a wide enough set of benchmark instances for the problem at hand, and ideally compare with other studies published in your field.
Finally, I recommend demonstrating the "added-value" of your new components by evaluating the performance of the same method when you change some key parameters or deactivate some of these components (this is sometimes called "ablation study"). This information is essential for future works since other authors are likely to re-use some components of your approach but not necessarily the entire algorithm. Therefore, they will wish to know what is the relative contribution of all the design choices that you made.
If you wish some examples of component analyses in hybrid algorithms, you can consult Section 4.2 of the following paper:
or Section 5.3 of this one:
Good luck!
--Thibaut
  • asked a question related to Metaheuristic Algorithm
Question
6 answers
Which Bio-Inspired Metaheuristics Algorithms are proposed in the last 8-10 years? How many of them are trending nowadays?
Relevant answer
Answer
As usual in this type of conversation (there are many others on ResearchGate), I recommend to read this position paper first, and then to take a minute to think if this diversity of "method names" makes any sense at all (other than generating an endless stream of papers).
  • asked a question related to Metaheuristic Algorithm
Question
9 answers
Hello!
I have a question if someone can answer me please.
As for the majorioty of metaheuristic algorithm, a step called initialization is a necessary for the methods. There are some ways to complete the initialization such as random initializtion which require seting the upper and lower bound. Now, I wonder if the metaheuristic algorithm could be applied for calculating the unbounded problem.
Relevant answer
Answer
Physical constraints can be modeled, surely.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
We know, In MATLAB software working in two types of environment. MATLAB Coding Script and MATLAB Simulink. For optimization smart grid operation many respected authors used different types of metaheuristic algorithms to solve this problem. The maximum author chose MATLAB Code to solve metaheuristic algorithms. Now my question: is it efficient to use MATLAB Simulink to replace MATLAB code to solve metaheuristic algorithms for optimization smart grid operation?
Relevant answer
Answer
The answer is both!
Either you can use codes to implement your study or do the same procedure in a simulator like Simulink. However, it depends on 'your' problem. If you are developing an algorithm, iterative procedure, optimization problem, or a study about a large network and agents with myriads of variables, parameters, and tunings, my advice is 'Matlab Code'. Otherwise, if you are studying on a small network in which you want to observe the operation and the responses of different components, and also you already can find all of them in the Simulink environment, you'd better use Simulink.
Good Luck!
  • asked a question related to Metaheuristic Algorithm
Question
38 answers
Hell, everyone. I am a student of electrical engineering and my research field is related to the optimization of a power system.
I know that the algorithm that we should choose depends on our problem but there are lots of heuristics, metaheuristic algorithms available to choose from. It will also take some time to understand a specific algorithm and after that maybe we came to know that the chosen algorithm was not the best for my problem. So as per my problem how can I choose the best algorithm?
Is there any simple solution available that can save my time as well?
Thank you for your precious time.
Relevant answer
Answer
As most people have indicated the best solution depends on the 'surface' you are optimising and the number of dimensions. If you have a large number of dimensions and a smooth surface then traditional methods that use derivatives (or approximations to derivatives) work well such as the Quasi-Newton Method. If there are a small number of dimensions and the surface is fairly sensible but noisy then the Nelder and Mead Simplex works well. For higher dimensions with noise but still farily sensible (hill like) then simulated annealing works. The surfaces which are discontinuous and mis-leading are best addressed with the more modern heuristic techniques such as evolutionary algorithms. If you are trying to find a pareto-surface then use a multi-objective genetic algorithm. So the key things are how many dimensions, is the surface reasonably smooth (reliable derivatives), do you want a pareto surface or can you run multiple single criterion optimisations. The other questions is, do you need to know the optimum or do you just want a very good result. There are often good algorithms for approximations to the best result, for example using a simplified objective function which can be found much faster to get a good rough solution which may be the starting point for a high fidelity solution. Sorry if this indicates it is complex, it really does depend on the solution space. Do not forget traditonal mathematical methods used in Operational Research as well. Good Luck!
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
Basically I get many research paper where are different types of metaheuristic algorithms used in the smart grid or the smart microgrid operation for optimization. I just want to know which algorithms are essential to optimize smart grid operation.
Relevant answer
Answer
This largely depends on your design problem. What aspect of smart grid operations do you want to optimize? What are your constraints criteria? Remember, smart grid operations is very wide. So, your design architecture dictates which optimization tool is applicable.
  • asked a question related to Metaheuristic Algorithm
Question
1 answer
Hi dear researcher,
i want to do the same optimization as the follows fminsearchbnd optimization with one of metaheuristic algorithm as GA:
clear all
clc
% % %%%read data
filename = 'D_test2.xlsx';
sheet = 1;
xlRange = 'A2:E60';
Mm = xlsread(filename, sheet, xlRange);
% % %Get the data
% % %X=(Mm(:,1:13)); %%% money ens data
HB=(Mm(:,2)); %%% hauteur brute
P=(Mm(:,3)); %%% puissance
QTU=(Mm(:,1));%%débit turbiné
Rg=(Mm(:,4));
k=0.00981;
m=(Mm(:,5));
x1=QTU(:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% options = optimset('PlotFcns',@optimplotfval);
%options = optimset('PlotFcns',@optimplotfval);
opts = optimset('fminsearch');
%opts = optimset('fminsearch',@optimplotfval);
opts.Display = 'iter';
%opts.TolX = 1.e-12;
opts.TolFun = 1.e-12;
opts.MaxFunEvals = 100;
sse =@(x)sum((x1 - x(3)./(k.*x(2).*x(4))).^ 2);
x0=[HB(:),P(:),Rg(:)]
n=length(x0);
LB=[36.5*ones(n,1) 50*ones(n,1) 0.73*ones(n,1)];
UB=[41.5*ones(n,1) 396*ones(n,1) 0.94*ones(n,1)];
%sse = fminsearch(sse,x0,options)
%xsol = fminsearchbnd(sse,x0,LB,UB,opts)
[xsol,fval,exitflag,output] = fminsearchbnd(sse,x0,LB,UB)
Here i expect to find a predictor vector which minimizes my objective function. I attached here a "D_test2" need in this programm.
Relevant answer
Answer
Daniel Eutyche Mbadjoun Wapet ,In MS Excel there is "The find a solution function" ("Поиск решения" (rus)). This function can find the minimum and the maximum of functions.
  • asked a question related to Metaheuristic Algorithm
Question
13 answers
Hi everyone,
Please, when you have an metaheuristic algorithm, how to forced decision variables to be integer?
Relevant answer
Answer
Hi, it is not possible to add a constraint in order to "force" a real variable to be an integer. More generally, in mixed integer programming you need to specify which variable is real and which one is integer.
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
Hi all, I have the following questions:
1) Does the solution with meta-heuristic (say genetic algorithm or similar) or reinforcement learning converges faster for the same optimization problem?
2) Is the solution obtained using reinforcement learning an optimal one?
Thanks in advance
Relevant answer
Answer
Some times the approaches maintain certain simmilarity
  • asked a question related to Metaheuristic Algorithm
Question
6 answers
If we considered the parameter as optimisation problem what are the available methods and what is the best way ?
Relevant answer
Answer
Through the use of generation number. The idea is to increase parameters that intensify the solution generation
  • asked a question related to Metaheuristic Algorithm
Question
15 answers
In problems with many local optima (multimodal) and many variables to optimize (multidimensional) which PSO variants are those that provide:
  • better exploration capabilities at the beginning of the search,
  • possibility of escaping local optima,
  • capabilities to find the optimal solution when it is not at the center of the coordinate system,
  • better quality of the final solution (more exploitation in the final period of the search process), and
  • low computational load (less evaluations of the objective function, shorter computation times)
It is grateful that in your response the bibliographic source where the PSO version is published is informed.
Relevant answer
Answer
All the answers are great
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
Is there any implementation of metaheuristic algorithms on social media data; specifically for recommendation system etc?
Relevant answer
Answer
All the answers are great
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
The new liste of thise ....
Relevant answer
Answer
Good point, Iago: in fact the veritable explosion of metaheuristics is a laughing stock among serious scholars. They are never (I think) compared seriously with traditional, that is, mathematical optimization, algorithms, but only among themselves, and it's a veritable jungle of weird methods with little - or no - theory behind them. I do not really understand why so many (at least at RG) spend so much time on this subject.
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
In reference point-based multi-objective optimization, the reference points are determined by DM.
What are the methods that can be used to calculate or determine reference points for a multi-objective optimization problem?
Relevant answer
Answer
Dear Mohamed-Mourad Lafifi , thank you for your answer. The references provide information about the methods that work based on the reference-point and sampling reference-points to be used with performance indicators in the evaluation stage. However, there is no information about how does the DM determine the reference points, for a particular problem, to be used with reference point-based (or preference-based) optimization algorithms.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Dear researchers
At my study, I am going to use the binary version of metaheuristic algorithm in the feature selection problem for a big data set (for example 1000 features or more). The metaheuristic algorithms such as binary PSO, binary ACO,...
But the point is that how can I select the relevant features? Whether are they automatically determined by the algorithm or they are fixed to the algorithm which can determine the best features with the same fixed value?
What I want to know is that is there any limitation to select the number of desirable features?
In advance thank you so much
B.R
Relevant answer
Answer
Just like what @Bishwajit Chakraborty , the determination process of the most minimum relevant features is called Feature Selection.
In order to that, you must determine the type of your dataset. Normal or large scale, in other words.. Small in terms of features, or v.large such as the datasets of DNA/Gens..
Usually, datasets with more than 1000 no. Of features is called large/ micro array dataset.
In order to determine the most relvant features, you choose which type.of feature selection method. There are three types, filter, wrapper, and embded. There is a new type which is a hybrid between the filter and wrapper approaches.
Wrapper Methods, are searching algorithm. In this type , you can apply a metaheuristic algorithm such as GA, PSO, ...
Let's say your chois is X algorthim. First, each solution in the swarm or population is encoded in a binary form. 1 represents a selected feature, while 0 represents a non-selected feature. The size of each solution is equal to the original no. of features. However, this not the only style you can apply in your research, you can represent your solution as a combinatorial problem.
The second step , you should choose a classifier , in order to evaluate the selected features. This step represents the fitness/objective function of the algorithm.
The rest steps are the searching behavior of the X algorithm.
Recently, most research papers are applying a hybrid filter/wrapper feature selection approaches.
The last question, I didn't get it..
Hope this helps,
Regards..
  • asked a question related to Metaheuristic Algorithm
Question
5 answers
How does Reinforcement Learning (RL) relate to Metaheuristic Search (MS) techniques like Genetic Algorithm, Particle Swarm Optimization, etc. and to Generative Adversarial Learning used in GANs? I feel that MS is similar to RL in the sense that it creates the next population based on feedback about the suitability of candidates from the present population based on function evaluations. Similarly, the generator and discriminator networks in GANs train based on feedback from the each other. What are your views? What are some other methods apart from MS and GANs that are similar to RL but not consider to be RL exactly?
Relevant answer
Answer
Dear Shounak,
It is a very interesting question. The Q-learning algorithm can be represented as an optimization problem (maximization problem), where the candidate solutions are the Q-values and the fitness function is the Q-function.
For more details about this topic:
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
I want to calculate inverted generational distance (IGD) to evaluate the performance of a multi-objective optimization algorithm. I have the approximate Pareto fronts. But, I could not find the true Pareto front for the structural engineering problems, such as, welded beam, spring, gear design problems,... etc. Any one has the data of true PF for them?
Relevant answer
Answer
You surely use some heuristic to find an approximate Pareto front. You will not able to find the Pareto front. To approach better to it, in the supposition that you generate a solutions population, you should to simulate the real behavior of your process, for each solution in the population to recalculate the the multiple objective function value and to reorder the found solutions. You won't reach exactly the Pareto front but you will approach enough and to obtain very good close to the Pareto front solutions
  • asked a question related to Metaheuristic Algorithm
Question
17 answers
It will be really good if the suggested journal doesn't spend much time in revision cycles, because I submitted this algorithm to "Applied soft computing" journal 1 year ago, and after 6 revision cycles, they just reject it with no real reasons.
Relevant answer
Answer
Hello, I suggest IJIETAP, Scopus indexed.
  • asked a question related to Metaheuristic Algorithm
Question
13 answers
As we know, exploration and exploitation are two important criteria in a population-based metaheuristic. I could find some formula to obtain a quantitative comparison of the exploration ability among different algorithms. but how we can compare the exploitation ability quantitatively among different metaheuristic algorithms?
Relevant answer
Answer
Hello Seyed,
You raise an important question which has no straightforward answer: how to adequately measure (and drive) the balance between diversification/exploration and intensification/exploitation in a metaheuristic. This question is recurrent in all methods, often a key for success, and connected to the topics of fitness landscape analysis and diversity management. Instead of going into exotic nature-inspired algorithms, as recommended in some previous answers, I recommend to take a step back for a technical analysis of the main components of the methods and problem structure. When focusing on a local search for example, it is meaningful to evaluate the ratio between the total number of solutions of your problem and the number of local minima. This ratio tells you how far your local search is able to "contract/project" the set of solutions into a smaller subset of local minima on which you will conduct the search. Naturally, this ability comes at a cost, in terms of CPU time, and you will have to find a good trade off between local-search complexity and solution quality. Here are a some other literature pointers connected to these topics:
Good luck !
-- Thibaut
  • asked a question related to Metaheuristic Algorithm
Question
1 answer
I've read something about that for plastic analysis and design of tall buildings,we can't use the VPS metaheuristic algorithm... is it a general limitation for all algorithm due to complexity of our problem and generating and combining the mechanisms or something else?
Relevant answer
Answer
you can use GWO algorithm it is simple and effecient
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Busco algun colega que pueda apoyar con la revisión de un manuscrito
Relevant answer
Answer
thanks simone this is about a review for the colombian EIA journal
  • asked a question related to Metaheuristic Algorithm
Question
8 answers
Hello Reseacher,
I have a science project in topic about metaheuristic algorithm which using Golden Section Search Algorithm, the characteristic of this algorithm is optimize 1 variable, how to optimize multi-variable same time?
Hope anybody help me, thankyou
Relevant answer
Answer
You may refer to the following paper:
Yang, J., Tian, Z., Wang, C., Ma, K., & Guan, X. (2017, October). A demand-side pricing strategy considering thermal comfort. In Chinese Automation Congress (CAC), 2017 (pp. 72-77). IEEE.
  • asked a question related to Metaheuristic Algorithm
Question
1 answer
can anyone show me how to writte a fitness function and duty cycle for MPPT by using any Metaheuristic algorithm?
Relevant answer
Answer
Dear Patrick,
I suggest you to see links and attached files in topic.
-An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems ...
-MPP detection of a partially shaded PV array by continuous GA and ...
-(MPPT) of photovoltaic (PV) systems - Wiley Online Library
-Multiple Peaks Tracking Algorithm using Particle Swarm ... - waset
-Training data optimization for ANNs using genetic ... - Tubitak Journals
-genetic algorithm for the maximum operating point in photovoltaic ...
-maximum power point tracker using genetic fuzzy controller ... - Revues
Best regards
  • asked a question related to Metaheuristic Algorithm
Question
4 answers
I want to simulate a routing protocol in the UWSN by using metaheuristic algorithm. We chose matlab as the simulator. We distribute the sensors in a 3D environment. we want to measure energy consumption.
We may need to code: Energy consumption model, Network lifetime, and Propagation speed.
What else may be required? I need this for implementation of UWSN routing scheme. Please advice. Any recommendations?
Relevant answer
Answer
I think underwater WSN by using radio link won't work. It has to be ultrasonic or optical link specifically with blue-green window.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Pareto solutions are found below true PF in case of 3D DTLZ1 problem. Whereas they are found above true PF in case of 2D DTLZ1 problem.
I have checked and confirmed problem formulation and variable bound. ( variables used are 3).
Algorithm converges on DTLZ-2, 3, 4.
What might be the cause or area of improvement in MO algorithm.
Reference: Deb K., Thiele L., Laumanns M., Zitzler E. (2005) Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham A., Jain L., Goldberg R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London
Relevant answer
Answer
Hi Leonardo,
I understand that you might be busy.
Please find time for answer.
Waiting for favourable response.
Thanks in advance.
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
My solution set in the Cartesian system placed in range x=0.5 and y=0.3 as diagonal line, but pareto set  is in range placed in range x=0.5 and y=0.5  as diagonal line.
Is it possible the solution set pass from the pareto set ?
or Is it possible the solution set being bether than  the pareto set ?
Relevant answer
Answer
following
  • asked a question related to Metaheuristic Algorithm
Question
10 answers
I try to find the better color space for edge extraction by color image
Relevant answer
Answer
Thanks to all of you. With a test image from Berkeley data-set I have used HSV color space with de-correlated images The segmetation results are quite good. Generally which indices do you use for the evaluation of segmentation quality?
  • asked a question related to Metaheuristic Algorithm
Question
3 answers
Can you recommend papers that prove theoretically that different versions of metaheuristic algorithms are actually giving similar performance? So far I have looked at the No Free Lunch (NFL) theorem and also read some papers that compare the performances, but these papers only report empirical results.
Relevant answer
Answer
 I am not familiar with theoretical comparisons of particular algorithms. Yet,  if you are interested in recent empirical ones then you may find some limited comparisons on a neuro-control problem in:
Salih, Adham, and Amiram Moshaiov. "Multi-objective neuro-evolution: Should the main reproduction mechanism be crossover or mutation?." Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on. IEEE, 2016.
.We are currently working on extending the paper to some other benchmark problems.
  • asked a question related to Metaheuristic Algorithm
Question
18 answers
I want a list of Meta-heuristics algorithms that consider the velocity vector to update the solution's position in the next iteration like: PSO, GSA, DA (Dragonfly) ... 
Relevant answer
Answer
Salem,
i have found these ones:
- Cockroach-Based Optimization
-Gravitational Search Algorithm
-Artificial Physics Optimization
-Kinetic Gas Molecule Optimization
-Wind Driven Optimization
all these metaheuristics and others can be found in the book entitled :
"Search and Optimization
by Metaheuristics
Techniques and Algorithms Inspired by Nature"
Best regards.
  • asked a question related to Metaheuristic Algorithm
Question
7 answers
I would like to know which is the best strategy for a in "incomplete" TSP. It is incomplete because I do not know  the reciprocal distances among the "cities" but I can evaluate the overall cost to travel across all "cities" (each city is visited, and only once etc..). I have to deal with ~3000 cities and the function to evaluate the path is pretty costly (~40 seconds) but I can make use of multiprocessing (I span computation on 20 processors). I have tried Tabu Search that shows I can improve solution respect to previous "greedy" attempts. Unfortunately Genetic Algorithms did not yield good solutions (the distance increases!!) likely because crossover and/or mutation inject noise instead of variable information. I used "ordered" crossover and I implemented mutation as swaps among "contiguous" cities. Maybe I would better go to Simulate Annealing but I am interested to know if for such a problem there might be a chance for GAs?! 
Relevant answer
Answer
You can use some machine learning methods to estimate the distances
  • asked a question related to Metaheuristic Algorithm