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# Swarm Intelligence - Science topic

Swarm Intelligence
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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?
Providing a proper balance between exploration and exploitation is a good choice.
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Hi, I am implementing path planning using PSO but I have no idea what would be the max and min values.
I have done some tests with arbitrary values but they only work for some cases.
Can you help me?
Maybe I'm a little late but...
In PSO, min and max values usually will define the algorithm search space. I'm not that familiar with path planning, but , In your case it looks like the search space is the minimum and maximum values the path can assume. That is, if the path goes from point (0,0) to point (5,5), that would be the min and max values.
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Hi, I have been researching about swarm algorithms and I need to implement some of them in path planning. I understand the algorithms, but I don't understand the process in which they are applied to path planning.
I have found some projects where they do it but I have not finished understanding the logic behind them. It is a bit confusing for me as I am a beginner.
Do you have any resources where you explain how to implement these algorithms in path planning? Can you share some codes?
Metaheuristic algorithm and machine learning - MATLAB Central
Engineering Optimization: An Introduction with Metaheuristic ... https://www.mathworks.com
metaheuristic-algorithms · GitHub Topics https://github.com › topics › metaheuristic-algorithms
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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?
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.
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In recent years, the field of combinatorial optimization has witnessed a true tsunami of so-called "novel" metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together -- it seems that no idea is too far-fetched to serve as an inspiration to launch yet another metaheuristic.
I would like to invite you to have a look on the following links.
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I need a list of journals SCI indexed based on application of swarm intelligence.
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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
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.
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The article, Simoiu, C., Sumanth, C., Mysore, A., & Goel, S. (2019). Studying the "Wisdom of Crowds" at Scale. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7(1), 171-179. Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/5271 notes that it is (p. 172):
“... one of the most comprehensive studies of the wisdom -of -crowds effect to date ..”
Are there any other comparable studies? If so, can you please provide the citations?
A different approach is to use emergent collectively solved problem sets, such as the English lexicon or rates measuring increases in efficiency for emergent collectively solved problem sets, such as :
1) The rate of improvement in domestic lighting: Nordhaus, W. D. (1994). Do real-output and real-wage measures capture reality? The history of lighting suggests not. Technical Report 1078, Cowles Foundation for Research in Economics, Yale University;
2) The rate of increase in average IQs. A theory of intelligence.arXiv:0909.0173v8;
3) By assuming a general collective problem solving rate, and finding a formula connecting the collective rate with the average individual rate of problem solving:
Kind Regards
Qamar Ul Islam
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Various metaheuristic optimization algorithms with different inspiration sources have been proposed in recent past decades. Unlike mathematical methods, metaheuristics do not require any gradient information and are not dependent on the starting point. Furthermore, they are suitable for complex, nonlinear, and non-convex search spaces, especially when near-global optimum solutions are sought after using limited computational effort. However, some of these metaheuristics are trapped in a local optimum and are not able to escape out, for example. For this purpose, numerous researchers focus on adding efficient mechanisms for enhancing the performance of the standard version of the metaheuristics. Some of them are addressed in the following references:
I will be grateful If anyone can help me to find other efficient mechanisms.
I recommend you to check also the CCSA algorithm implemented by a Conscious Neighborhood-based approach which is an effective mechanism to improve other metaheuristic algorithms as well. The CCSA and its full source code are available here:
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What are the standard parameter values of the commonly used classifiers such as Support-vector machine, k-nearest neighbors, Decision tree, Random forest?
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Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]
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Is the using algorithms in finding interested area in images fruitful?
Medical Image Registration Using Evolutionary Computation: An Experimental Survey By: S. Damas; O. Cordón; J. Santamaría
Analyzing Evolutionary Algorithms: The Computer Science Perspective
By: Thomas Jansen
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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.
The 7th section, namely "Results, Data Analysis, and Comparison", of the following current-state-of-the-art research paper have a sufficient answer for this question:
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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.
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:
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Dear All,
What kind of small mobile robots are available in market to undertake multi-robot research? Would you have anything to be recommended?
Thanks!
Chaomin Luo
~~~
Chaomin Luo, Ph.D.
Associate Editor of IEEE Transactions on Cognitive and Developmental Systems
Associate Editor of International Journal of Robotics and Automation (SCI-indexed)
Associate Editor of International Journal of Swarm Intelligence Research (IJSIR)
Associate Professor
Department of Electrical and Computer Engineering
Mississippi State University
312 Simrall Bldg., 406 Hardy Rd., Box 9571
Mississippi State, MS 39762
USA
I have read about the smart dust robot. It is still under investigation. You may go though , the project has a lot of limitations like ( power consumption, communication overhead, processing cost. Somehow you can get your aim.
regards
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Local search method helps to increase the exploitation capability of optimization and meta-heuristic algorithm. It can help to avoid local optima also.
A hint: it is not OK to be ignorant of nonlinear mathematical optimisation. First you absolutely need to understand optimality criteria - as those conditions are the goal to fulfill. Mathematical optimisation tools are DESIGNED such that accumulation points from the iterative scheme WILL satisfy the optimality criteria, and in most cases you will be able to see the "fault" in the optimality criterion shrinking.
In contrast metaheuristics have no such "guiding light" at all - they fumble in the dark! Few sensible scholars with knowledge in mathematical optimisation would use these tools.
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What is the effect of increasing or decreasing population size and the number of iterations on the quality of solutions and the computational effort required by the Swarm Intelligence algorithms?
The increase of the population leads to (at least initially) increased diversity of the population, but if one increases the population size too much, there may be slow convergence of the population to the global optimum. But if the population is too small, it will lead to entrapment to local optima. It is widely suggested to increase the population in order to avoid local optima, especially if the objective function has many parameters. As for the number of iterations, an increased number will increase the possibility for convergence to global optimum, but you may start doing useless computations. In this case, you might need a good termination criterion that prevents useless computations after reaching global optimum.
For the latter, read the following paper.
Spanakis, C., Mathioudakis, E., Kampanis, N., Tsiknakis, M., & Marias, K. (2016). A Proposed Method for Improving Rigid Registration Robustness. International Journal of Computer Science and Information Security, 14(5), 1.
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Am looking for latest work on swarm intelligent
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...
Dear Long Nguyen Cong,
Can you able to generate Levy random number in the interval [a, b] using Levy distribution.
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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).
Hi
It is usually convenient for an optimization algorithm to obtain the optimal solution when the optimal point is in the symmetric search space. When the optimal point of a test function is shifted, the search space goes out of symmetry. Therefore, the power of the algorithm in detecting optimal solutions may be reduced.
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New hybrid techniques helps to reduce the energy consumption in routing that needs to be identified .I am reviewing this .
Jaya optimization in WSN
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I'm doing some research on dimensionality reduction using swarm intelligent algorithms. As per the no free lunch rule, there is no algorithm that best suit all the problems. So, to be able to find the best subset I need to determine whether it's unimodal or multimodal? The data is of 300 features and 1000 instance. Is there any visualization methods that can help in this regard?
Dear D\ Ahmed
No general methods for dimensionality reductions because of the various of data characteristics. To overcome this limitation, you can use ensemble learning (as, ensemble feature selection). It supports the diversity and stability terms. I recommend you this paper " Ensembles for feature selection: A review and future trends":
This book for more details "Recent Advances in Ensembles for Feature Selection",
Also check this paper " Swarm Intelligence Algorithms for Feature Selection: A Review
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Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the opposite relationship among entities. This concept is used to initial population that randomly generated.
I think Its really depend on how you initialize the population.
I assume that you randomize population S with n solution, then take the opposition population S^ based on S. Now, there are two possible ways you can do here is:
+ 1st: Compare each solution in S and S^ with their respective index. Take the solution with better fitness.
+ 2nd: You concatenate S and S^ then take n solution with the best fitness.
Both ways have the weakness is: Maybe, all position of all solution placed at the 1 side of search space. Especially, when that side belongs local optima, It will make your algorithm fast convergence (but in local optima)
I think the best way to use the opposition-based technique for the initial population is:
You randomize population S with n/2 solution, then create n/2 solution left with opposition-based technique. This way make sure that all position doesn't fall in the 1 side of the search space.
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What is the most efficient way to measure the impact of the adjustment of each hyperparameter of a given Evolutionary Algorithm with many hyperparameters? Is there any way to graphically visualize it? If not, how can we do it numerically?
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Swarm Intelligence (SI) techniques are more and more used by researchers in a wide range of areas: physics, economy, materials, biomedical sciences, computer sciences, engineering...
Recently, new published papers propose enhanced variant of SI techniques to improve the optimal solutions and achieve better results than other conventional optimization techniques, namely ACO, PSO, differential Evolution (DE)...
Does anyone have worked on these enhanced variant of SI techniques?
Regards.
Amin
I have already proposed several improved versions. This book Will be published soon:
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I was going through mac protocols and wondering what will be a suitable MAC protocol for UAV swarm while conducting a mission.
Any idea?
Dear Anik,
I suggest you to see links and attached files on topic.
Paper Title (use style: paper title) - laccei
Survey on Unmanned Aerial Vehicle Networks: A Cyber ... - arXiv
Survey of Important Issues in UAV Communications Networks - arXiv
Best regards
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Switching topology means that the topology of the agents switch into a different topology over time. Time-varying formation happens when the formation pattern changes over time for several different reasons. Can we interpret that topology switching is a unavoidable consequential step in time-varying formation?
I think switching topology case can be considered as a subset of time-varying formation/consensus. Consider the following two cases for discrete time consensus:
(i) When the topology of a network is switching, the system matrix W[k] (row-stochastic for discrete-time) will be different for some k>0 such that w_ij[k] > 0 does not mean w_ij[k'] > 0 for some k' ≠ k.
(ii) Suppose that topology is fixed but agents of a network use different weights for some k that results in time-varying system matrix W[k]. In that case, if w_ij[k] > 0 holds, then we have w_ij[k'] > 0 for some k' ≠ k.
Similar arguments are also valid for Laplacian matrix L(t) (zero row sum for cont. time). Hence, while (i) represents the switching topology case, time-varying formation/consensus case might be represented either (i) or (ii) depending on the problem.
Hope this helps.
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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?
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
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I am looking forward to start my Master's thesis and I am not sure it is a very good idea to combine Blockchain with either swarm intelligence or machine learning !
There is already some papers on it.
One example:
At the moment, it does not sound very promising but I think eventually these two fields will be married.
But again, we have to find a proper application for this marriage. All I have seen until now is some made up application which does not sound convincing. Maybe your Masters Thesis will change that.
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To find the more perfect approximate value where exact value is difficult to find.
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Ant colony optimization (ACO) was first proposed by Dorigo et al. as a multi-agent approach applied to the classical traveling sales man problem (TSP) [Dorigo M., Maniezzo V. and Colorni A. Positive feedback as a search strategy. Report No.: 91-016. 1991]. In support of deep learning and deep neural networks where hundreds to thousands of collaborative neural layers consume billions of operations, and cannot be operational unless the efficient and optimized corporation of a multiprocessor or multicore CPU with a thousandcore GPU [Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295-2329, 2017].
Hi Eduard,
My first recommendation is to choose a proper platform and programming language suitable for Concurrency , distributed computing and scalability , easy to implement NN. Erlang is what I am using at the moment, I found it is perfect for this type of implementation; any other platform would cost you enormous amount of investment to get anywehere.
Then you need an architecture from bottom-up. scalability is very important in the solution your seeking.
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Iam working on medical data prediction using evolutionary algorithms and stuck on data classification .Now iam seeking help for this
See this:
I'm not able to sent code for you, but this should be quite easy to re-implementation.
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Hello,
Please, who can help me to find a forthcoming Journal special issue in Manufacturing, scheduling, optimization, swarm intelligence .. ?
Thank you
Dear Prof Amal
Neutrosophic logic application
With best regards
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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
Hi Leonardo,
I understand that you might be busy.
Waiting for favourable response.
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Swarm intelligence (SI) techniques are population based algorithms that are inspired by the social behavior of animal.
1.- Computational cost: SI-based meta-heuristics are relatively easy methods to implement.
Normally this type of algorithms performs searches in continuous spaces, and two approaches can be used to use them in discrete spaces: 1) Modify the operators that update the particles in the swarm, or 2) to define the scheme to map a candidate solution of the problem in a particle of the swarm.
These approaches impact the performance of an SI-based method.
2.- Performance metrics: This depends on what you want to measure: time, space, precision, for example. It also depends on the problem that is being solved.
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I am looking for new research direction in cooperative control of multi-agent systems. What are the latest trends in this field of study? any comment is much appreciated.
Dear Samira Esheghi,
of course i am agree with our colleague Luy Tan Nguyen and I add that exist many papers about this topic using differents techniques. For more proof i suggest you to see links and attached files in this topics.
-Cooperative Control of Multi-Agent Systems - Optimal and | Frank L ...
- Distributed Cooperative Control of Multi-agent Systems
- Cooperative Control of Multi-Agent Systems: Theory and Applications
- Cooperative Control of Multi-Agent Systems: Theory and Applications ...
- Cooperative Control of Distributed Multi-Agent Systems
Best regards
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I would like to find out any novel way to design an algorithm that can be used to manage and monitor intermodal transportation and supply chain
I have developed algorithm and railway traffic management system inspired by bee foraging technique and decision theory. maybe you can find out more from the papers that i have published
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Every year more than 10 different swarm optimization algorithms are published. Are they really good?
Hi, I recently published a paper entitled " Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach", so-called SRSR optimization algorithm. I also shared MATLAB code of this algorithm as well as 19 other well-known heuristic evolutionary algorithms in form of one free package for the sake of a fair comparison on the Internet. Based on my experiences, each algorithm can be suitable for a specific set of problems based on its inherent features and formulation; in terms of convergence quality and convergence speed. If an algorithm has outstanding performance on some problems does not necessarily guaranty that can be outstanding for other problems as well. Many people believe that some algorithms like PSO are completely dated and you should not apply them to any problem anymore; However I found that traditional PSO has a better performance than several new algorithms in some special case-studies. Finally, there is no other option in my idea, you should test a bunch of algorithms to find best one suited to your problem. Moreover, some of algorithms have a good performance in small-scale problems and others are better in case of large scale. In my vision, quality of an algorithm is 100% percent related to the problem you wanna to apply on. Some time, some algorithms suffer from low pace because their computational cost is high, however they can find very high quality solutions. On the other side, they may have high convergence speed with lower convergence quality.
From another point of view, publication of more than tens evolutionary algorithms during a year, as you said, may assist other researchers to find some novel more applicable ideas in case of applied soft computing; because this is a step-by-step evolutionary procedure. Some time the best option is to simply combine to algorithms enjoy abilities of both ones and solve your problem; however it is not a new optimization algorithm.
Kind regards
Mojtaba
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Can I use TSP real problem with the modified continues algorithm (ABC,FA,ANt) in a article
What a real problem do you mean?
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I proposed an improved Firefly and compare it with variant of Firefly and sent it to a journal after getting major and minor revision, one of the four reviewers said me should be compare with another metaheuristic as well.
I viewed some papers when they proposed the new modified algorithm, they tried to compare with variant (like firefly) of it not other metaheuristic (swarm intelligence and evolution algorithm).
The other thing, one of the reviewer said me comparing swarm intelligence and evolution algorithm are obfuscate and we cannot compare them with to gather.
Should I compare it or not ?
yes you can do that , i advise you to perform two tests also the first one is t test and other one is w test. very easy and efective to measure and execute
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I have two data sets and I want to use these data sets to tune the parameters of particles swarm optimization (PSO) algorithm using machine learning method.
Hello,
Samer Sarsam,
Thank you so much to have pointed out this.Yes its true, PSO is one of the methods that can be used to optimize SVM. Infact, similar work has been done by me wherein i have used SVM as a base classifier and thereby incorporated ACO, PSO and Flower Pollination (Swarm based methods) to optimize the base classifier SVM.
SVM does not optimizes PSO, and I have not mentioned that, however SVM can be used to control some of the parameters of PSO or ACO or FPAB. As i have mentioned in the last line
"Depending on what parameters to be controlled (Inertia weight, acceleration constant, Control exploration or something else)"
The answer i has written earlier was in context to: For instance, SVM are capable of delivering high performance in terms of classification accuracy but their proficiency truly relies on an optimal choice of hyper-parameters. On the other hand, Control parameters selection of PSO has no theoretical guidance, most choices of PSO are based on experience.
That is wat was intended..of course SVM is not an Optimization technique, it is just a learning model that can be used as a base classifier. Thank you for sharing the article.
Regards,
Rebika Rai
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Swarm intelligence and Evolutionary computation
I am working on finding the optimal path using ant colony.
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blind source separation include many methods to separate the mixed signals as independent component analysis.
there are many methods used to enhancing the separated signals as particle swarm optimization.
Are there ability to using the Quantum Particle Swarm Optimization in this field?
if u can use PSO then u can use QPSO too but for me Quantum is used to enhance the pso method but the ability of quantum is as heuristic as PSO. so it depends on what kind of result u r depicting from yo algorithm.
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Two years of observations on oil palm trees plantations in Malaysia had shown novel nesting behavior of the Asian weaver ants Oecophylla smaragdina (Fabricius) that was never reported.
These contained an average of 3.98 ±1.74 (mean ± SD, range 1-13) nests per tree with the only odd number of nests in each surveyed trees.
The phenomena exist during both dry and rainy seasons of the year. In the biological system, only one case is clearly reported in North America for the cicadas insect eaten by birds when with a life cycle of 13 or 17 years, which still remain a mystery. The ants exhibited polydomous nesting behaviour, as reported by other authors (Debout et al. 2007), with multiple nests in a single palm tree, and multiple queens were sometimes observed in the main nest, suggesting polygyny (Exélis Pierre and Azarae, 2012- in press).
Four experimental design testing had shown all positive results demonstrating that there are factors regulating the mechanism, from the queens. How and why? it is yet to be found out...
I would like to know if the modeling equation system could help to explain the underlying biological mechanism regulating this. Beside the swarm intelligence of these ants. Any ideas or suggestions are welcome.
Nesting behaviors of O. smaragdina in oil palm plantations
Objective:
How a colony of O. smaragdina distributes its workforce and territory iIs a key behavior in the biological pest management using this ant.  There are two processes (1) nest-allocation to each tree (how many nests are constructed in a specific tree), and (2) worker-allocation to each food items (how many workers take part in when they bring food item) and each part of the territory.
Methods:
(1) Nest-allocation
a. Our preliminary study suggests a strange phenomenon that we observed in each oil palm tree whereby the ant constructs the odd number of nests. We examine if this phenomenon does really exist (not only on oil palms but on other trees).
b. If the above phenomenon exists we experimentally study its mechanism by manipulating the number of nests in the field.
(2) Worker allocation
a. Examine the relationship between the size of the food item and group size by video especially focusing on its movements.
b. Monitor the daily activity of foragers in the field to know when they hunt what items on what time?
A national survey is conducted on the total number of nest occupying each palm trees in Peninsular, Sabah & Sarawak, related to a unique phenomenon of oddness number of nests. The survey is carried out during both rainy and dry season of the year.
Direct counting is done using a binocular device for tall palms.  This is a novel study on an exceptional behavior rarely seen in nature. The phenomena of odd prime number exist only among the insect periodical cicadas Magicicada septendecim, M. septendecula, and M. cassini in North America with a life cycle of emergence after 13 or 17 years (Fontaine et al., 2007). Little is known on this unique factor related to the odd number of 13 and 17 (it is never 12 or 18; even numbers). A recent report related the cicadas timing of emergence to the abundance and synchrony of avian predators populations in the USA. Cuckoos (Coccyzus spp.) increased highly during emergence years to decline consequently profusely while red-bellied Woodpeckers (Melanerpes Carolinus), Blue Jays (Cyanocitta cristata), Common Grackles (Quiscalus quiscula) and Brown-headed Cowbirds (Molothrusater) had their population abundance increase for 1-3 years subsequently to cicadas emergence to finally decline (Koenig & Liebhold, 2005). What are the underlying biological mechanisms responsible for this phenomenon? in the case of O. smaragdina nesting behaviors in oil palm plantations.
To investigate the underlying biological mechanism, to understand this, performing the following experiments might lead to verify the existing nesting behavior among Oecophylla smaragdina ants devising the tendency of constructing an odd prime number of nests per palm tree.  From the previous survey of occupancy:
I-                   By adding a nest to a tree that already has a nest, and observe what happen (if the populations of the added nest are always absorbed to the old one?).
II-                Or release a few collected but disturbed nests on the base of a palm tree where already a nest exists on the canopy.
III-             From these, the expected hypothesis is - Ants whose nests were collected and disturbed may climb up to the nearest palm tree to reconstruct a new nest.  However, on the tree, there is already a nest belonging to the same polydomous colony.  If there is any mechanism that regulating the nest numbers leading to the odd prime number phenomenon, ants should never reconstruct a nest, but if they do they will make 2 or 4 new nests in both cases
IV-             . Another trial is to destructively cut a frond of a palm tree having i.e 11 nests to artificially reduce the total nest number to 10. In that case, if there is a real apparatus and variable responsible or explaining this mysteriousness, the ants shall rebuild at least one new nest to rebalance the tendency of odd nest number to obtain again 11 nests in total. It is possible that three more nests would be added for a final total of 13 nests.
The trial is statistically analysed by Chi Square test X2, and data are verified by Fisher’s exact test or Barnard’s exact test which is a more powerful alternative of the previous for samples size n < 1000. Barnard’s exact test can verify the strength of the data record on odd prime total nest number per palm tree obtained.
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Hello,
I am working on Fitness case importance for Symbolic Regression and found a Paper  "Step-wise Adaptation of Weights for Symbolic Regression with Genetic Programming" which talks about weights of fitness cases  to give importance to points which are not evolved to boost performance and also get GP out of local optima.
This publication is too old and i am looking for new work which talk about fitness cases importance. But i am not able to find any such publication. Instead i find Publications related to sampling on Random selection in different ways.
So, Can someone point me towards research works relevant to Importance or Weighting Datapoints like SAW(Stepwise adaptation of weights) technique?
Thank you.
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I'm using Nature Inspired Intelligent algorithm a lot for my research. One problem that I've faced so far is the correction of solutions to match an interval [a,b] .
For example, when testing the performance of such an algorithm in a benchmark function like Goldstein–Price function which has a search domain [-2,2], algorithms (mostly Swarm Intelligence) violate this ..constraint.
One solution is to use a penalty function.
But what am I seeking for is some kind of normalization to match the values to the right search domain.
Note that a solution is a vector of values in different characteristics of the problem. So I cannot use the simple normalize of an array (http://stackoverflow.com/questions/10364575/normalization-in-variable-range-x-y-in-matlab).
Thanks a lot!
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Hi everyone,
I am trying to build a Neural Network to study one problem with a continuous output variable. A schematic representation of the neural network used is described in the figure below.
[Figure 1: Schematic representation of neural network: input layer size = 1; hidden layer size = 8; output layer size = 1.]
Is there any reason why I should use the tanh() activation function instead of the sigmoid() activation function in this case? I have been using in the past the sigmoid() activation function to solve logistic regression problems using neural networks, and it is not clear to me whether I should use the tanh() function when there is a continuous output variable.
Does it depend on the values of the continuous output variable? For example:
(i) Use sigmoid() when the output variable is normalized from 0 to 1.
(ii) Use tanh() when the output variable has negative values.
Normally we normalize input variable to reduce the chances of getting stuck in local optima. It also makes training MLP faster  When inputs are normalized in range of [0 1] use logsig() activation function and when in range of [-1 1] use tansig() activation function instead  In your case (function approximation), you can use linear activation function (purelin) activation function for output neuron. For other applications (e.g., classification, clustring) you better use sigmoidal (tansig or logsig) activation functions for all layers (hidden layer(s) as well as output layer). Good luck.
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Hello everyone,
I have recently opted for the COCO framework for comparing and analysing continuous algorithms to deeply analyse an algorithm I am working on.
The paper of Hansen et al. (coco: performance assessment) was of great help for understanding the rationale of the adopted result analysis procedure in the case of a mult-algorithm comparison. However, some details remain still unrevealed, hence:
1. Could anyone cite some references talking about the categories of functions cited in the benchmarks, namely: separable fcts, moderate fcts, ill-conditioned fcts, multi-modal fcts, weakly structured multi-modal fcts ?
2. The three types of graphs, offered when comparing only two algorithms, have not been presented in the above-mentioned reference, see attached file.
Hence, I would be really thankful for any clarifications.
Don't hesitate to contact me if you need any help. My main research topic is continuous black box optimization, within the TAO team ( same team of authors of the BBOB/COCO testbench )
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What behavior of which animals/birds/insects shows Swarm intelligence. And what are the practical aspects of using them to solve different problems?
As Dr. Ramon López de Mántaras pointed out Bee or Ant colonies foraging is a behavior that is simulated to solve optimization problems. Grey Wolf Optimizer(GWO) algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature.  Particle Swarm Optimization (PSO) simulates the social behavior of bird flocking or fish schooling which is used to solve different optimization problems. For more information please see the following links:
Regards,
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Hello,
I am trying to do a hybrid of two swarm intelligence algorithms to form a new algorithm which will be applied in task scheduling integrated with cloudsim tookit.
i hope someone could help me ,recommend me some papers or source code
Thank You
Dear Moodys,
Here are link and attached files in subject.
-A Hybrid CS/PSO Algorithm for Global Optimization - Springer
Best regards
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Apart from economic frameworks like oracle or sql server, I am looking for academic business intelligence data to conduct some experimental researches such as applying data mining algorithms or optimization algorithms. Can suggest links for such data?
Regards,
Rafid Sagban
Hi Rafid,
I work with AI methods. They excellently apply on function generation and data prognosys. I am not familiar with any application based on this methods for Enterprise Business Application testing. My suggestion is to make one for youreself, based on AI methods (Fuzzy, c-slusters, neuron network, ANFIS).
good luck
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In recent time, there are a number of research work have been published on enhancing the search capability of evolutionary and swarm intelligence based algorithms by employing a chaos based local search technique. How does this chaotic local search actually affect the performance of the algorithm in terms of exploration and exploitation? Also, what are the key factors that should be kept in mind before designing such kind of local search process?
The local search can be very destructive or very constructive. It depends on the type of problems that is attempted to be solved.  It is worth exploring whether or not the chaos can bring something positive to the search or not. The "no-free-lunch" theorem suggests that one techniques may be optimum with one problem, but its performance may be degraded for another. So it is worth to bear this in mind.
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Let's assume we have a standard feedforward ANN with just a single  hidden layer. It is standard practice to normalize the input data ,usually in [0,1] or [-1,1]. Let's assume min-max normalization. If we have sigmoid activation function, wouldn't it be more sensible to normalize in a range like [-4,4] or [-5,5] ? The sigmoid function is in essence linear in [-2,2] so if we normalize in [-1,1] the approximated function is linear for the most part. It might be argued that for certain weights the input for the sigmoid activation function can be outside the normalized range but still that's generally few cases (depending also on what values the weights might take).
As for how to initialize the weights: A common formula is in the range [-b,b] where b = 1 / sqrt(Ninput+Nhidden) (assuming sigmoid function).
It is usually said that small weights are more preferable (also if you like at regularized error functions that penalize large weights ) since large weights have higher chances of leading to overfitting.
Any thoughts?
Hi Nikolaos,
In my experience, normalization procedure is used in NN input if the different features that are being used are in a different numeric value/ranges. The normalization is done feature-wise usually, so the impact on the network would be similar.
The sigmoid function is not applied directly to the input features, I mean, if we have X = [x_1, ... , x_n] as input an W_i = [w_i1, ..., w_in ] the synaptic weights corresponding to the i'th neuron of the following hidden layer, the activation of that neuron w.r.t. the sigmoid function would be:
Y=sigmoid(X · transpose(W_i)) that it's always between the range (0,1).
If you start with very large values in the input and weights, the sigmoid signal would be 'crushed' to 1.
There are several ways to initialize the initial weights, but small random numbers is usually fine. At the end, these weights will be updated according to an optimization function to complete the designed task.
Hope that some of this information helps you.
Cheers.
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Actually, a large plethora of muli-objective metaheuristics is used in the available literature. Among these optimization approaches, the newly proposed Swarm Intelligence techniques (MOPSO, MOACO, MOBFO..), and the Evolutionary Algorithms (MOGA, NSGA-II, AMOSA...) are the most used ones.
• How can we compare Pareto fronts of each metaheuristic?
• What type of methods or mathematic metrics can we use to classify these metaheuristics?
There are many examples of quality measures in the literature. Some aim at measuring the distance of an approximation set to the Pareto-optimal front: Van Veldhuizen [21], e.g., calculated for each solution In the approximation set under consideration the Euclidean distance to the closest Pareto-optimal objective vector and then took the average over all of these distances.
Other measures try to capture the diversity of an approximation set, e.g., the chi-square-like deviation measure used by Srinivas and Deb [18]. A Further example is the hypervolume measure which Considers the volume of the objective space dominated by an approximation set [26]. In these three cases, an approximation set is assigned a real number which is meant to reflect (certain aspects of) the quality of an approximation set. Alternatively, one can assign numbers to pairs of approximation sets.Zitzler and Thiele [26], e.g., introduced the coverage function which gives for a pair (A, B) of approximation sets the fraction of solutions in B that are weakly dominated by one or more solutions in A.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: an analysis and review. Evolutionary Computation, IEEE Transactions on, 7(2), 117-132.
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I recently read an excellent paper detailing the problems with metaphors in the design of metaheuristics:
The components are relatively easy: a solution representation, a fitness function to evaluate solutions, and methods to create new solutions.
Key strategic components include means to avoid and/or escape from local optima, balance exploration and exploitation, etc.
Where do people stand on these? I'm particularly interested on insights into cooperative coevolution that involves solving multiple problems in lower dimensions. How does this work for non-separable problems? Other than some optimization is better than no optimization, what's the justification/insight?
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I am looking for Java code for ACO (Ant colony Optimization)- based Feature extraction. I want to adopt the strategy on my Dataset which has real (Numeric) attributes and a binary nominal class attribute. Does anyone know how can I find such a code?
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In recent years lots of papers were published about ant algorithm-, and swarm intelligence applications on logisitcs.  But are there any further findings for logistics provided by biomimetics?
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I am a PhD student in computer science . The object of my thesis is "solving optimization problems in cellular networks". It consists of resolving optimization problems that exist in radio mobile networks using bio-inspired algorithm called "metheuristics". Any comments regarding this?
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After the success of the Artificial Bee Colony algorithm for optimization tasks of local search algorithms, Artificial Neural Networks is among the most irresistible research areas in the field of Soft Computing. Furthermore, researchers have developed attractive algorithms based on nature intelligence behavior Like CS, BA, DSA... etc. The one research article "Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster," is going to publish in Springer. This paper was successfully used to predict earthquake hazard with high efficiency. Can we say that nature intelligence algorithms can solve optimization problems?
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One problem Firefly algorithm being trapped in local optimality. How can I fix this problem by combining the simulated anneling algorithm ?
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I am working on an idea of complexity reduction for unconstrained optimization problems.
I want to test the idea on some derivative unconstrained real-world optimization problems. It seems not easy to find such real-world optimization problems.
Can someone suggest any?
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I am trying to used the improved method of the said algorithm but I have experienced some difficulties any idea, Engineers?
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The context is in the analysis of various techniques of optimization done using particle swarms and with also using methods like granular computing which could be applied keeping in view the advantages.
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Hello!
I'm currently revising my optimization algorithm for a specific part of a problem. I have trouble in wrapping my head around a new approach and my mind is having this tunnel-vision of ideas. I could really use some fresh perspectives.
I'll try my best to simply the explanation.
(Please see attached file "Example-1.png" and "Example-2.png")
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Say, we are given 3 distinct persons.
Each of these person have a specific Supply (an item that he/she possess) and a Need (an item that needs to be acquired). Now, if these Supply-Need is reversed, and their reverse can be found in another person, they can be traded.
Moreover, the pairs have a numerical value called Gravity that specifies the importance of the pair to the person. We can treat it as a weight on how much a person can be "satisfied" if the Supply-Need pair is met. Each person can only allocate and distribute 100-points of Gravity among all of his/her Supply-Need pairs.
Now, the Total Satisfaction of this process can be computed by getting the sum of all the Persons' individual satisfactions.
The objective is to have the group of Persons trade their Supply-Need pair/s in different combinations such that we can acquire the largest Total Satisfaction as possible.
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In PSO, each Particle represents a candidate solution based on its location in the Search Space.
Given the attached examples in this post, we can say that Example-1.PNG is a distinct candidate solution to this problem, as well as Example-2.PNG.
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What's the best approach in how this problem can be represented and evaluated by a Fitness/Objective Function?
How would you characterize this problem in PSO?
Do you have any recommendations of published work with the same problem as this?
Cheers!
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Are this techniques good for tuning PID and adaptable in industrial environment?
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Hi, I am trying to implement Particle (or Genetical) Swarm Optimization. However, I am already stuck in the first step...
I am getting confused on how to initialise the particles, and what these particles (in terms of code) are.
Thanks.
Andrea.
The simplest way to represent a particle is a vector. For example, in an optimization problem with 3 design variables, each design (particle) is represented by a vector [x1, x2, x3]. So each particle is such a vector and represents a point in the 3D space (that we all know and can imagine). For problems with dimension > 3 it is difficult to imagine the particle and its position, but with N=3 you get the idea.
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Please suggest the method for mobile actuator network
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I want a matlab code that shows how to make integration between any sI algorithm like chicken with a genetic algorithm as a wrapper feature selection method and then use the reduced subset for data classification using svm-knn-c4.5 and so on.
the problem in the hybridization step (how to mix two algorithms together : sequential - parallel or what)
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Examples such as those found in ants, bees, bacteria etc. point to the fact that collective decision making is also an important aspect of decentralized decision-making process. Most bacteria utilize quorum sensing to maintain population density, ants use it to find new nests, and recently, robots and self-organizing systems are using this signalling mechanism for decision-making. This forms the basis of Swarm intelligence. Incorporating some of these signalling aspects of SI into AI systems would make them more efficient and 'intelligent', do you agree?
How 'artificial swarm intelligence' uses people to make better predictions than experts?
"Dr. Louis Rosenberg, CEO of Unanimous AI, is building a software platform, UNU, that assembles groups to make collective decisions. "What's different about this is that it fundamentally keeps people in there," he said. "We're focused on using software to amplify human intelligence."...
UNU is an online platform where anyone can log in and answer questions as a swarm. "People benefit when they form a real-time system the way bees, fish, and birds do, as opposed to people just casting a single vote the way people do," said Rosenberg..."
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A textbook or classic are both fine. It is much better if it has pedagogical approach with a focus on optimization and algorithms implementation.
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Want to have implementation details (code if possible) for solving job shop scheduling using bio-inspired algorithms like ant-colony optimization, genetic algorithm, cat swarm opt. etc. Especially want to know how to set initial parameters like number of iterations & other parameters like number of ants in case of ant-colony optimization etc.
Hi, I am also working on this problem using PSO. In fact, number of particles/ants or number of iterations depends upon your problem. You can experiment by giving different values of particles and by setting different number of iterations and check results. Initially, you may set small number of iterations and small number of particles.
You should balance between number of particles and number of iterations.
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