Abstract
In evolutionary algorithm, population diversity is an important factor for solving performance. In this paper, combined with some population diversity analysis methods in other evolutionary algorithms, three indicators are introduced to be measures of population diversity in PSO algorithms, which are standard deviation of population fitness values, population entropy, and Manhattan norm of standard deviation in population positions. The three measures are used to analyze the population diversity in a relatively new PSO variant—Dynamic Probabilistic Particle Swarm Optimization (DPPSO). The results show that the three measure methods can fully reflect the evolution of population diversity in DPPSO algorithms from different angles, and we also discuss the impact of population diversity on the DPPSO variants. The relevant conclusions of the population diversity on DPPSO can be used to analyze, design, and improve the DPPSO algorithms, thus improving optimization performance, which could also be beneficial to understand the working mechanism of DPPSO theoretically.
Highlights
Particle Swarm Optimization (PSO) is a kind of bionic evolutionary algorithm proposed by Kennedy and Eberhart in 1995 [1]
The results show that the three measure methods can fully reflect the evolution of population diversity in Dynamic Probabilistic Particle Swarm Optimization (DPPSO) algorithms from different angles, and we discuss the impact of population diversity on the DPPSO variants
Kennedy proposed a kind of PSO without the velocity attribute [12, 13], Ni and Deng did some further research [14, 15] and systematically integrated a kind of PSO variant, Dynamic Probabilistic Particle Swarm Optimization Algorithm (DPPSO), and many variants of DPPSO showed better solving performance
Summary
Particle Swarm Optimization (PSO) is a kind of bionic evolutionary algorithm proposed by Kennedy and Eberhart in 1995 [1]. Kennedy proposed a kind of PSO without the velocity attribute [12, 13], Ni and Deng did some further research [14, 15] and systematically integrated a kind of PSO variant, Dynamic Probabilistic Particle Swarm Optimization Algorithm (DPPSO), and many variants of DPPSO showed better solving performance. As for this kind of PSO, there are not so many researches in population diversity, population topology, and parameter settings.
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