Learning Competitive Swarm Optimization.

Bożena Borowska

doi:10.3390/e24020283

Abstract

Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

Highlights

Academic Editor: Shu-ChuanSwarm intelligence (SI) is a branch of artificial intelligence (AI) based on the social behavior of simple organisms occurring in natural environments [1]
To evaluate the effectiveness of the proposed learning competitive swarm optimization algorithm (LCSO), the test results were compared with those achieved through the competitive swarm optimizer (CSO) [21], comprehensive particle swarm optimizer (CLPSO) [47], PSO [51], fully informed particle swarm (FIPS) [35], the covariance matrix adaptation evolution strategy (CMA-ES) [52] and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) [53]
The use of sub-swarms helps maintain the diversity of the population and keep the balance between the global exploration and local exploitation; Particles learning from the winners can effectively search space for a better position; Good information found by sub-swarm is not lost; Particles can learn from the useful information found by other sub-swarms; In each iteration, the position and velocity of only two out of three particles is updated which significantly reduces the cost of computations; Particles do not need to remember their personal best position; instead, the competition mechanism is applied; LCSO can obtain better results and convergence than the other algorithms

Summary

Introduction

Swarm intelligence (SI) is a branch of artificial intelligence (AI) based on the social behavior of simple organisms occurring in natural environments [1]. In order to improve the performance of the particle swarm optimization method, Cheng et al [21] introduced a competitive swarm optimizer (CSO) based on PSO. This leads to a deterioration in the effectiveness of the method and premature convergence To reduce these inconveniences, ensure diversity of particles and limit the risk of getting stuck in the local optimum, in this paper, a new learning competitive swarm optimization called LCSO is presented. The proposed approach is based on the particle swarm optimization method (PSO) and a competition concept. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO) [21], comprehensive particle swarm optimizer (CLPSO) [47], PSO [51], fully informed particle swarm (FIPS) [35], the covariance matrix adaptation evolution strategy (CMA-ES) [52] and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) [53]

The PSO Method

The Proposed LCSO Algorithm

Results

Conclusions