Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

Abstract

The particle swarm optimization (PSO) algorithm is a swarm intelligence (SI) algorithm used to solve optimization problems. Owing to its advantages in simplicity, using only a few parameters, PSO has become one of the most popular optimization algorithms. However, the single structure of PSO leads to challenges in finding the appropriate optima, resulting in low convergence accuracy. To solve the defects of PSO, it is necessary to increase the diversity of the populations involved as well as enhance the ability of the algorithm to develop locally. In this study, we propose a PSO algorithm with an adaptive two-population strategy (PSO-ATPS), which adaptively divides a population into two groups representing excellent and ordinary populations. Inspired by animal hunting behavior, a new velocity–position update method is proposed for the general population. A velocity update formulation with decreasing inertia weights based on logistic chaotic mapping is applied to the excellent population. The algorithm increases the diversity of the population by continuously changing the search strategy of the particles. In addition, a new neighborhood search strategy (oscillation strategy) is proposed, in which a particle searches randomly in its own adaptive neighborhood when its motion is stagnant and updates the particle position using an elite strategy. Among several optimization strategies, PSO-ATPS achieved first place in 7, 8, and 9 groups of tests involving 10 test functions in 3 dimensions, indicating the accuracy and effectiveness of PSO-ATPS. The results show that the performance of PSO-ATPS is competitive, and many improvements developed for PSO can be applied to PSO-ATPS, demonstrating the potential for further development.