Affiliations: [a] College of Economics and Management, Northeast Agricultural University, Harbin, China
| [b] Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang, China
| [c]
Guizhou Province Big Data Industry Development and Application Research Institute, Guiyang, China
Correspondence:
[*]
Corresponding author. Fu Yan, E-mail: yanfuphd@163.com.
Abstract: Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC’14 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems.
Keywords: Autonomous groups of particle swarm optimization, particle swarm optimization, levy flights, dynamically changing weight, function optimization
