In multi-objective context using the pareto optimality it is desirable to generated all the non dominated solutions on the pareto front. PSO is a very popular evolutionary algorithm that uses individual and social experiences of particles to generate the candidate solutions.
I wanted to know what can be a good method for choosing a candidate for GBest(best group experience) in MO-PSO where it is desirable to generate all solutions on the pareto front.
Thanks in advance.
You need to define a pseudo objective function which considers the pareto optimal front and also the diversity of solution at the same time.
Here is a review paper on MOPSO. Go through this paper, some guidelines can be found in the associated literature. Hope this will help you.
http://www.softcomputing.net/ijcir/vol2-issu3-paper5.pdf
Attached articles are good references in MOPSO:
Sierra, M. R., & Coello, C. A. C. (2005, January). Improving pso-based multi-objective optimization using crowding, mutation and∈-dominance. In Evolutionary multi-criterion optimization (pp. 505-519). Springer Berlin Heidelberg.
Elhossini, A., Areibi, S., & Dony, R. (2010). Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evolutionary Computation, 18(1), 127-156.
Li, J., & Yu, G. (2013). Multi-objective Particle Swarm Optimization Algorithm with Chaotic Mutation. International Journal of Advancements in Computing Technology, 5(5).
http://www.aicit.org/IJACT/ppl/IJACT2443PPL.pdf
look at this paper
http://www.sid.ir/EN/VEWSSID/J_pdf/8542010B305.pdf
Two recent articles on MOPSO:
Curtis, M., & Lewis, A. (2015). Reduction of Computational Load for MOPSO. Procedia Computer Science, 51, 2789-2793.
Luo, H., Chen, M., & Ke, T. (2015). Multi-Objective Particle Swarm Optimization Algorithm Based on Comprehensive Optimization Strategies. In Advances in Swarm and Computational Intelligence (pp. 479-486).
In the MOPSO, proposed by Coello Coello, an external archive, called Repository, is used to select the leader (GBest). The selection scheme is based on the Gridification of the objective space, just like the other multi-objective optimization algorithm, the Pareto Envelop-based Selection Algorithm II (PESA-II).
Open-source implementation of both algorithms in MATLAB, is available to download in the following links:
MOPSO: http://yarpiz.com/59/ypea121-mopso
PESA-II: http://yarpiz.com/86/ypea123-pesa2
MATLAB programming codes for PSO
https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization?ev=prf_pub
https://www.researchgate.net/publication/280244004_A_comparative_study_of_metaheuristic_optimization_approaches_for_directional_overcurrent_relays_coordination?ev=prf_pub
Code Codes in MATLAB for Particle Swarm Optimization
Article A comparative study of metaheuristic optimization approaches...
Kindly, go through the following publication.
Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, R. Harley, Particle swarm optimization: Basic concepts, variants and applications in power systems, IEEE Transactions on Evolutionary Computation, 12 (2) (2008) 171-195.
I hope it will be very helpful to you.
Regards,
M.N.Alam
https://www.researchgate.net/publication/297245624_Particle_Swarm_Optimization_Algorithm_and_its_Codes_in_MATLAB
Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB
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