Entropy Diversity in Multi-Objective Particle Swarm Optimization
AbstractMulti-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.
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Pires, E.J.S.; Machado, J.A.T.; de Moura Oliveira, P.B. Entropy Diversity in Multi-Objective Particle Swarm Optimization. Entropy 2013, 15, 5475-5491.
Pires EJS, Machado JAT, de Moura Oliveira PB. Entropy Diversity in Multi-Objective Particle Swarm Optimization. Entropy. 2013; 15(12):5475-5491.Chicago/Turabian Style
Pires, Eduardo J.S.; Machado, José A.T.; de Moura Oliveira, Paulo B. 2013. "Entropy Diversity in Multi-Objective Particle Swarm Optimization." Entropy 15, no. 12: 5475-5491.