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Optimization Algorithms - Science topic
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Questions related to Optimization Algorithms
Which tuning method is optimal for adjusting PID controller parameters, such as Ziegler-Nichols (ZN), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)? Can you provide an overview of each tuning technique for the manipulator?
I am working in fuzzy graph and its application.
I want to use it in optimization of PID controller's parameter, I do not know how implement and coordinate them by using MATLAB.
in mathematical modeling of whales encircling prey in (2.2)-(2.3) (paper in
Article The Whale Optimization Algorithm
), the search agents update their location accordance to the best agent. That is, the search agents approach the prey by the amount A.D when A>0 or move away by the same amount when A<0. But I could not understand the role of coefficient C. If there were no coefficient C, wouldn't there still be approach or going away?Thanks
How do I evaluate accuracy of different meta heuristic algorithms such as Chimp Optimization algorithm, Spider monkey optimization algorithm? I came across a term called to Bonferroni Dunn's test but unable to find any related related tutorials to perform that test. Can someone please shed some light on this?
How to people propose a new optimization algorithm. I mean what is the baseline? Is there any intuition or mathematical foundation behind it?
After getting the results of any Topology Optimization Algorithm, how could we generate the CAD/CAM model?
Knowing that the algorithm will give the optimum shape in a form of matrix containing the pseudo density (a number between 0 and 1).
I have the following paper:
Nidhal 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.
Nidhal El-Omari, “An Efficient Two-level Dictionary-based Technique for Segmentation and Compression Compound Images”, Modern Applied Science, The Canadian Center of Science and Education, published by Canadian Center of Science and Education, Canada, p-ISSN:1913-1844, e-ISSN:1913-1852, DOI:10.5539/mas.v14n4p52, 14(4):52-89, 2020.
How can I add them to the Google Scholar?
I am working on the development of a PMMS model. To select the best-performing tools and models, several models are needed to be developed and validated. Can this be replaced by some optimization algorithms?
Metaheuristics are a class of Artificial Intelligence algorithms. There exists several metaheuristics to solve a variety of optimization problems. The list is lengthy enough. Researches are in search of more efficient algorithms, and hence proposing newer metaheuristics. Sea Lion Optimization Algorithm is one such metaheuristics. The algorithm is claimed to outperform Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA) and Dragonfly Algorithm (DA). Can anyone please provide some sources for implementing the Sea Lion Optimization Algorithm in any programming language?
(A)What is Exploitation and Exploration in Optimization Algorithms? (B) Describe the local and global search for the PSO, GA, ABC and ACO algorithms and compare them.
Dear Prof. Tishko N. Muhamad,
I have the following paper:
In this paper I said the following paragraph which may be the solution to your valuable question about Exploitation and Exploration:
Fig. 2 exhibits that the searching process falls into quadruple pivot points: stochastic, exploration versus exploitation, iterated (i.e. iterative), and guided:
- The searching has a stochastic nature when the set of random variables is employed in extracting a new generation, called a potential solution [9][16]. Each generation's new values of these variables are chosen stochastically in harmony with the general paradigm. On the other side, some problems are evolving stochastically at different points in time which makes the optimization hard to grasp and solve. For instance, the passengers' numbers of airlines occur stochastically which calls the airlines for implementing the statistical theories, including stochastic analysis and probability distributions, in forecasting the numbers of passengers.
- Exploration and exploitation are two supplementary activities used to explore the search space. The first one is the activity through which the search algorithm tries to explore as much broad search space as possible to evade falling in the local-optima traps. Whereas the second one is representing the activity in which the searching algorithm tries to develop the finest discovered solutions through some targeted approaches. While exploitation is the optimized outcome derived out of exploration, the progress of the search for more solutions continues in both cases to find more-optimal ones. [10][17][18]
- For highly efficient exploiting the former iteration (also called trial or time-step) to the greatest possible extent, the outcomes in the guided search are frequently improved over the course of the iteration steps where the newly generated trials are influenced by the older ones. The basic idea of that is wrapping around deliberating knowledge and extracting patterns from the former good-quality searching iterations in an effort to harmony guide the searching process in the subsequent iteration steps hoping to be in the optimal solution direction, hence the notion of “guided” is used to continuously approximate the goal based on replacing the old solutions with the new successful ones. From a more general angle, the useful information related to the optimas of the preceding iteration steps is stored on to get benefit from them in the succeeding ones. That is, the key philosophy of deriving more successful trials is coupled with building up a well-stocked knowledge store containing the past trials. [10][17][16]
- Unlike the conventional local search that stops when it gets stuck by any local optima, the guided local search makes the best use of the available features of the current optima to escape away and then form another more optimum feasible solution. In the guided search absence, an optimization algorithm (OA) inevitably takes on a longer exploratory time range that is mainly driven by the trial-and-error aspects. [10][17][18]
- - The progress of the searching process will be continuing iteratively with the same searching procedure until one or more of the predefined evaluation functions, called termination conditions or stopping criteria, imposed by the user are reached, and accordingly, the best possible candidate solution is produced. Without that, the progress of the searching mechanism will obviously be in an infinite loop [10][17]. The followings are some of the possible termination conditions that may be imposed by the user to terminate the series of iterations: the maximum number of iterations steps previously defined (i.e. no. of runs) is reached, the maximum allowable CPU computational run-time (i.e. max-CPU-time), coming across some significant evidence that an optimal solution has been achieved, the maximum number of iteration attempts that comes amid two successive developments is reached, the maximum number of iteration attempts has reached without noticing any difference or making any forward positive progress for the problem of interest, or there is no way to get more developments for the problem of interest (i.e. there is no progress) [10][17]. Related to the first termination condition, the applications of the Artificial Neural Networks (ANNs) use the name “epoch” to represent an iteration step (i.e. time-step) [10][18]. In-line with the second termination condition, it's very crucial to balance between the quality of given feasible solutions and the overall computation time frame that is utilized in generating these solutions and decide accordingly how to set up the timeout bound [10][18]. On the other hand, the last termination condition represents the case that after many generations, the solutions start approaching each other in the hope that this approaching is a good indicator that the final achieved solution is closer to the ideal solution of the problem under research [10][18]. In the report of this, there is an essential need for suitable criteria to define the quality of the acceptable solution and to decide according to that whether to stop the searching activity or not [10][17][18]. If the procedure fails to reach a visible solution or a practical compromise within the timeout bound, it is inevitably stopped [10][18].
If anyone has, please send it to me or share it with me.
I would like to compare my algorithm (the improved LPA) with Louvain, infomap and CNM (fast greedy) algorithms (available in Mat lab toolbox community) that has been implemented on LFR dataset.
I confronted with a problem when I cannot use the outputs of the algorithms for NMI criteria.
I will gratitude everyone could guide me about the matter!
Dear All,
I have successfully applied NSGA-II on a multi-objective optimization problem.
While I was observing the results during the optimization process, I have noticed that some variables (genes) have reached very good values that matches my objectives, while others haven't. However during the optimization, the good genes are being frequently changed and swapped to undesired results (due to genetic operations; mutation and crossover) until the algorithm reaches a good local optima, or hits a condition.
My question is this:
Can I exclude some genes from the optimization process since they have already reached my condition sand finally combine them with the remained genes in the final chromosome?
There is an idea to design a new algorithm for the purpose of improving the results of software operations in the fields of communications, computers, biomedical, machine learning, renewable energy, signal and image processing, and others.
So what are the most important ways to test the performance of smart optimization algorithms in general?
I have executed a optimization algorithm 'A' towards an objective.After 50 independent runs i have got 50 values.I have executed a optimization algorithm 'B' towards the same objective.After 50 independent runs i have got 50 values. i need to compare the performance of two algorithms. ranksum or signedrank wilcoxon is best to appply?
I have to solve a optimization problem. I already find Pulp library at Python but I want to solve it with metaheuristic algorithm. My problem involves with discrete decision variables and constraints, minimization objective function. I can't decide which algorithm go better with my problem. Also, I need a similar code for it.
I am using the ANN for a product reliability assurance application, i.e.picking some sample within the production process and then estimating the overall quality of the production line output. What kind of optimization algorithm do you think works the best for solving the ANN in such a problem. ?
I observed that some algorithms obtained very small fitness values of the given optimization problems like 10^-150. and some of proposed methods obtained 10^-190. They are very small numbers. What is the significant difference between 10^-150 and 10^-190.
As we know, computational complexity of an algorithm is the amount of resources (time and memory) required to run it.
If I have algorithm that represents mathematical equations , how can estimate or calculate the computational complexity of these equations, the number of computation operations, and the space of memory that are used.
i have two questions sir the first one Kindly give design procedure for FOPID Controller and Give some sample m file program.
second one How to design FOPID controller tunning methods by using optimization algorithms
Pls reply me sir
I am preparing a comparison between a couple of metaheuristics, but I would like to hear some points of view on how to measure an algorithm's efficiency. I have thought of using some standard test functions and comparing the convergence time and the value of the evaluated objective function. However, any comments are welcome, and appreciated.
There are many research on metaheuristic optimization e.g. Particle Swarm Optimization, Genetic Algorithm, etc. Some study show that they are good for clustering task. But I don't find any comparation about it.
Which one is the best to be applied for optimizing the clustering process?
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
I am working on a closed-loop system which has a PI controller. I have made a Simulink environment for the overall scenario but now I want to optimize the coefficients of the PI controller through an optimization Algorithm which will be having a Matlab code. So, can anybody help in this regard?
I am trying to implement the grey wolf optimizer for analyzing data of different brain tumour patients across a specific region but I am unable to exploit the algorithm for my work. I intuitively feel that a swarm algorithm can provide me a good data analysis.
Local search method helps to increase the exploitation capability of optimization and meta-heuristic algorithm. It can help to avoid local optima also.
I am trying to schedule the VMs into host machines using one of the optimization algorithms (ant colony or GA or ...) to find optimal solution for energy consumption SLA aware. Can someone sugests ideas?
Hello,
I have some more multi-objective optimization questions using the exhaustive search method(brute-force method) that help would be much appreciated. An answer to all or one of the questions are all very very welcome.
I have 3 different multi-objective optimization problems with 3 discrete variables.
Optimization Problem 1) 2 objective functions
Optimization Problem 2) 2 objective functions + 1 constraint
Optimization Problem 3) 3 objective functions
The ranges for the variables are 15~286, 2~15, 2~6, respectively.
I have been told that the search space is small enough that exhaustive search space is my best bet and that it is easy to implement so I wanted to try it.
My questions are
1) Is it possible to apply the exhaustive search method to all three optimization problems?
2) How would I go about doing this using a computer software?
I was thinking that for
Optimization Problem 1 and 3
I would find all the function values first and then move on to finding and plotting the pareto fronts -> Is this the right approach?
Also, is there any example code I could follow(especially for optimization with three objectives)
For Optimization Problem 2 with a constraint
How would I incorporate this constraint?
Just make the program give me the function solution values that do not violate the constraint (meaning ignoring solutions that do) and than use them in plotting the pareto front?
Is there example codes/programs for doing this?
I would in particular find any Matlab and R codes helpful.
If there is a specific program/software or what not that does this, I would be very grateful as well.
Thank you to everyone in advance!
Hi Guys,
I need to write a N-dimensional Downhill Simplex Algorithm. Does one of you happen to have a implementation for Matlab which I could use as a reference?
Thanks a lot in advance!
I have learnt that to implement different task scheduling policies (like RR, FCFS, ACO, SJF), I need to make changes to SubmitCloudlet method in DataCentreBroker class. However, I am having trouble coding the Round Robin Task Scheduling Algorithm. While in VMs, do we have to include Time Quantum or we just assign cloudlet to VMs in a Round Robin way.
Could you please send me the code or help me out with it.
I'm struggling with my qualification work. Excel methods solve the math problem easy, but I don't catch the idea how he does it. I need to understand it to create the code or just to describe the algorithm with help of block-diagrams.
The issue is that I have a mix of continuous function and discrete vars.
I attach two files: the first contains the function which is need to be minimized and constraints, the second one is excel file with the solution.
Multiple Meta Heuristic Optimization Algorithms like Grey Wolf Optimizer face a problem of Shift In-variance, i.e. when optimum of an optimization model is at (0,0), the algorithm performs quiet well. However, when the same model is shifted by some coefficient, the performance of the same algorithm goes to drain.
An example might be taken from f1 & f6 of standard Benchmark Functions (CEC2005).
Hello ... the objective here is maximizing Z for each product (i)
function [ Zi ] = myfitness( X )
P=X(1);
C=X(2);
Q=X(3);
% Zi= fitness value
% C,P,Q = variables vectors
for i=1:10;
Zi = P(i).*Q(i)-C(i).*Q(i);
end
end
the outputs should be a 1*n matrice
when i run the function it works but i get only one value , and doesn't work with ga toolbox i keep getting the same error (index exceeds Matrix dimensions) ....how can i fix this error?
any help would be appreciated ....Thank you
The Quantum Approximate Optimization Algorithm is a very promising way to have a trade off between results correctness and speedup. My question is how to implement this practically by having this closed loop feedback between a quantum and a classical processor? Can a simulator be used instead of a quantum device?
How I can implement the SWARM Optimization Algorithm, prefer Java Code, to maximize an objective function with three variables? as shown below:
Y= 2+2.3*X1-.3*X2+.1*X3
1< X1 <3
10< X2 <100
30< X3 <100
Why dual algorithm is used in optistruct to solve the topology optimization? Why they are not using Optimality Criteria method?
Hi,
We developed a subpixel image registration algorithm for finding sub-pixel displacement and I want to test that against existing methods. I have compared that with subpixel image registration algorithm by Guizar et al. and also the algorithm developed by Foroosh et al. Does anyone knows any other accurate algorithm for subpixel image registration (preferably with an open-source code)?
Thank you.
"Sperm Swarm Optimization Algorithm"
I am currently working on a project which requires self tuning of BELBIC controllers parameters i.e PID gain and Learning rate of Amygdala and Orbitofrontal Cortex. I need some suggestions as to how I could integrate optimization algorithm i.e. PSO to tune these parameters for a 3rd order NON-Linear System. I know how PSO works, the only thing is, I am having difficulties in linking BELBIC and PSO together.
This is a follow up thread to https://www.researchgate.net/post/What_is_the_optimal_number_of_restarts_for_a_fixed_number_of_function_evaluations?
The goal is to develop general guidelines that can lead to the improved performance of (population-based) metaheuristics on (continuous domain) multi-modal fitness functions. Assuming a fixed or constrained number of function evaluations, our plan is to use multiple restarts of a metaheuristic tuned to converge relatively fast.
Our current focus is to identify and restart at the "critical" search scale. In general, on (benchmark) multi-modal fitness functions, there are rapid improvements in fitness at the beginning (as the search scale is larger than the attraction basins of the local optima and the search process rapidly explores the overall global structure of the search space), an "elbow" (which we believe could be around the "critical" search scale), and then slow improvement as mostly local search occurs to find a local optimum.
We are looking for features that we can identify in real-time that indicate this "elbow" so that we can restart at it and thus have the new metaheuristic procedure spend more time and effort at this "critical" search scale. We believe that this transition from coarse global search to the specific selection of an attraction basin to exploit will heavily affect the performance of metaheuristics on multi-modal fitness functions.
Any ideas?
I want to use the differential evolution technique in order to get rate constant at given reaction conditions.
I am using differential evolution (DE) for estimation of parameters in a model. If a parameter value lies between 0 to 1, the optimization algorithm has ideally infinite values between 0 and 1 to look at. If I were to restrict the decimal to be just one, there will be just nine values the algorithm needs to visit. I am not sure if rounding the parameters in each iteration, in mutation, cross over,etc. is going to be efficient. Please advise me if you have any idea.
Thanks,
Ahammed
I want to use Particle Swarm Optimization (PSO)for finding hyper parameters of a support vector regression problem. Initially I tried to find the same using grid search method,but the Matlab code is taking too long to produce results. Even after reading a lot on PSO, I am still not clear on how to apply it. Can anybody help me understand or refer me to a good text which outlines how step by step PSO can be used in my case.
Which way do I proceed? Nothing that I read helps me make that decision
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.
in Matlab GA solver when i try to solve my objective function it displays Too many output arguments. how to resolve this error. i try different problems with different variables . in each time it displays the same. i want some suggestions from anybody.
with regards
L.mamundi azaath
Cuckoo search clustering algorithm.
Backtracking Search Optimization Algorithm (BSA) is one of the recent meta-heuristic algorithms. In spite of its success, it might have some drawbacks. What are the main drawbacks of BSA?
Any one who can help me with Collective Animal Behavior (CAB) Optimization Algorithm MATLAB?
Hi, I want to know if there is a way to combine various ensembles using Multi Objective Optimization Algorithms in Matlab. Can body please point me in the right direction ?
Thank you in advance.
Please refer this paper to know what are the problems they have performed the test.
How to perform recommendation system with Optimization algorithm like ant colony algorithm?
i'm trying to optimize a simple 2D truss structure using optimization solver.
the objective function is total strain energy of the structure (mod1.truss.Ws_tot) and the constraint is total weight (mod1.mass1.mass).
control variables are a set of three length parameters of position of truss members and cross section area.
I read everything available on comsol documentation about optimization, but i still couldnt figure out how to use derivative based optimization algorithms to this model.
I think the problem is that my objective and constraint is indirectly related to control variables but i dont know how to connect them in the right way.
1- in neb does the force act on each image or each atoms in the
2- if the force acts on each atoms,is it the same for all atoms in each image?
3- is the interpolation between images linear? if yes, how to understand that the path is the best when there are diffrent paths between initial and final images? (Becuase different MEPs exist between initial and final images)
4- How does optimization algorithm determine the best path? Is there any criterion ?
Hi,
Can anyone explain it to me "what is Velocity in PSO and how to calculate Velocity in PSO". Explanation with small example will be appreciated!
Regards
Muddsair
Hello
I would like to ask about Chaotic Optimization Algorithms. Why most chaotic optimization algorithms using the initial value of chaotic map equal to 0.7?
Hi there.
Recently I focus on the link scheduling problem in wireless sensor network, especially in IEEE802.15.4e Time Sync Channel Hopping (TSCH) networks. The key of the scheduling is to decide the order of the links, a.k.a different oder result in different performance such as average delay time.
The problem maybe related to permutation optimization，somebody suggest stochastic optimization(like simulation annealing) or genetic algorithm or swarm optimization. While these method requires high computation cost.
Any other feasible idea?
I have installed contiki framework for my experiment, what is the best techniques to implement query optimization algorithm ??
I am using accelerated bender decomposition algorithm which lower and upper bound reach convergence before second iteration ,is it normal such as case?
,as I have attached file,it is shown after iteration 1 lower and upper bound convergence,what happened ?,in fact when I use classic bender decomposition in iter num 2 lb and ub are convergence even with large size,I try to increase size of my problem however classic BDA is convergence in second iter so in this case is it worth to use accelerated BDA?
under which criteria does SSO and DE algorithms come?
Can anyone suggest me some references (preferably papers or articles) that discuss the sensitivity of computational intelligent optimization algorithms, more specifically soft computing techniques, to an initial solution?
It seems, regardless of the type of the techniques, e.g. evolutionary, swarm, network-base, etc. the quality of ultimate solution of some techniques is affected by the initial solutions while others show less sensitivity. Please let me know if you have any comment, suggestion or information on this topic.
Scenario: Randomly deployed sensor nodes with finite energy source each sensing some information from close surrounding and transmitting it to base station for further processing.
Problem formulation: Select some node as a cluster head such that they are energy rich and well distributed in the field. Well distributed so that they minimize the inter-cluster distance and energy rich so that they can be Cluster head for a long duration of time (highest energy node should be selected as cluster head in order to reduce the frequency of re-clustering because if the energy of cluster head goes below a threshold we will change the cluster head ).
I need a centralized algorithm which will be executed on Base Station and given the current location and energy state of all nodes it will select some node as cluster head, such that the cluster heads are well distributed and energy rich. The algorithm should balance both the factor in such a way that the energy of nodes must be saved up to the maximum possible level.
In the SIM reconstruction algorithm, it is necessary to separate the measurement results, but separation need to know the exact translation phase of illumination stripes. I wrote a phase calibration procedure according to the method of literature, but the calibration results are not accurate. I would like to know whether there is a large necessary to calibrate the phase? If necessary, what needs to pay attention in the calibration algorithm? How to remove the measurement error superimposed on the results?
Hello everyone,
I am trying to remove this absolute operator for cplex.
Zi=1/2 | ∑_(j=1)^J▒(-1)^j (x_(i,j) - x_(i+1,j)) |
where Zi has be {0,1}
but if I remove the mode, the values I am getting is {0,1,-1}.
Xi,j is Binary decision variable
How can I replace the absolute operator to make it an equation for Mixed integer Linear programming.
PFA word file to clearly understand the equation.
any suggestions please.
Thank you.
I am doing an optimization with the genetic algorithm. I have bounds and variable constraints. Then from literature; I found that we have to add constraints as Penalty function. But I could not understand what penalty function is clearly. And how to add penalty function to GA optimization?
Hello All
I'm using Genetic algorithm to solve multi objective problem optimization. Like any optimization algorithm the solution should be the minimum of the objective function value, but if I want to add a constrain on the minimum value. Assume I have two objectives Y1 and Y2, I want to make the stopping criteria like this
*if Y1<z and Y2 is the minimum value
stop*
Is it possible?
Thanks in advance
Regards
Mansour Alramlawi
Still papers are coming using weighted sum approach for solving bi-objective problems even though Pareto optimal algorithms such as NSGA-II are popular.
how to explain the concept of Pareto front using weighted sum approach. Is it possible to draw Pareto front curves using weighted sum approach.
require an explanation supported with the steps for evaluate the RSA and AES algorithms in cloud computing , by using google engine or any cloud engine . the procedure to encrypt the data by algorithms (text file) and uploading / downloading to/from the google engine and then methods to evaluate the performance of algorithms in different metrics .
Is there any methodology to find proper parameter settings for a given meta-heuristic algorithm, eg. Firefly Algorithm or Cuckoo Search? Is this an open issue in optimization? Is extensive experimentation, measurements and intuition the only way to figure out which are the best settings?
I am using PSO for document clustering , i implemented pso simple algorithm but i have problem with implementing the objective function of pso for document clustering and then i extend PSO algorithm for Multiview documents clustering .any body help me about the simple objective function of PSO for document clustering.
I am looking for a method to compare the dataset represented in blue with the one in red and I need to extract one single value from this comparison. The idea is to compare datasets generated with different combinations of parameters to a 'optimal' dataset and get a 'score' of each one so I can see which combination of parameters is the closest to the optimal model. I came up with a few options like Fréchet distance, Hausdorff distance and MSE but I don't know which one would work best for me.
Does anybody have any suggestion or another method that could work?
Thank you.
