Entropy Diversity in Multi-Objective Particle Swarm Optimization

Entropy

Eduardo J. Solteiro Pires²  José A. Tenreiro Machado¹ 

[1] ISEP—Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering, Rua Dr. António Bernadino de Almeida, 4200–072 Porto, Portugal; E-Mail:;INESC TEC—INESC Technology and Science (formerly INESC Porto, UTAD pole), Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000–811 Vila Real, Portugal; E-Mail:

关键词: multi-objective particle swarm optimization;    Shannon entropy;    diversity;   

DOI  :  10.3390/e15125475

来源: mdpi

【 摘 要 】

Multi-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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.