Adaptive Particle Swarm Optimization with Gaussian Perturbation and Mutation

Binbin Chen,Long Chen,Shengjie Long,Rui Zhang,Wei Li

doi:10.1155/2021/6676449

Abstract

The particle swarm optimization (PSO) is a wide used optimization algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed the improvements to address the drawbacks above. Most of them have implemented a single strategy for one problem or a fixed neighborhood structure during the whole search process. To further improve the PSO performance, we introduced a simple but effective method, named adaptive particle swarm optimization with Gaussian perturbation and mutation (AGMPSO), consisting of three strategies. Gaussian perturbation and mutation are incorporated to promote the exploration and exploitation capability, while the adaptive strategy is introduced to ensure dynamic implement of the former two strategies, which guarantee the balance of the searching ability and accuracy. Comparison experiments of proposed AGMPSO and existing PSO variants in solving 29 benchmark functions of CEC 2017 test suites suggest that, despite the simplicity in architecture, the proposed AGMPSO obtains a high convergence accuracy and significant robustness which are proven by conducted Wilcoxon’s rank sum test.

Highlights

Particle swarm optimization (PSO) is an evolutionary computing technique proposed by Kennedy and Eberhart in 1995 [1], originating from the simulation of predation and other behaviors of bird flocks and fish schools. e solution of each optimization problem in the algorithm is similar to a “particle” in the search space. e particle swarm algorithm randomly generates an initial swarm and gives each particle a random velocity
In order to improve the solving ability of particle swarm optimization, researchers have proposed methods, such as an adjustment of the inertial parameters of particle swarm algorithm, including dynamic policies and adaptive methods, learning factors, and social factors [2], a neighborhood searching strategy to strengthen the exploration of the neighborhood of the current population [3], an adoption of the information-sharing mechanism to enhance population diversity and avoid premature algorithm convergence [4], and the integrations with other algorithms, such as the combination of particle swarm optimization algorithm and immune algorithm, genetic algorithm, and artificial bee colony algorithm [5]
To prevent trapping into local optimum, Gaussian perturbation is implemented to global optima, further increasing the exploitation capability

Summary

Introduction

Particle swarm optimization (PSO) is an evolutionary computing technique proposed by Kennedy and Eberhart in 1995 [1], originating from the simulation of predation and other behaviors of bird flocks and fish schools. e solution of each optimization problem in the algorithm is similar to a “particle” in the search space. e particle swarm algorithm randomly generates an initial swarm and gives each particle a random velocity. (3) Identically, according to the adaptive strategy, the mutation is implemented to improve the diversity of particles that have stagnated evolution and to balance the ratio of inheritance and mutation to ensure the population’s searching ability. Once the optimal particle falls into the local optimum, it hardly escapes To address this problem, we incorporate the Gaussian perturbation and mutation strategy where the threshold stop_num is set to define whether the particles are in the evolutionary stagnation state or not. When a particle falls into the evolutionary stagnation, the mutation operator is introduced into partial dimensions in the speed updated equation (6), which increases the diversity of the population getting rid of the constraints of gbest particles, especially improves the search ability of particles with low speed due to converging near gbest, and promotes the particle utilization. AGMPSO can maintain higher diversity throughout the early period and lower diversity in the later period, which ensures both the global search ability and the convergence

Experiments and Discussions

Conclusion