An Adaptive Particle Swarm Optimization Algorithm Based on Aggregation Degree

Title:An Adaptive Particle Swarm Optimization Algorithm Based on Aggregation Degree

Volume: 11 Issue: 4

Author(s): Xiuli Zhang*, Ruihua Zhang, Jianping Wang and Laidi Wang

Affiliation:

• College of Electronic and Information Engineering, Zhejiang Wanli University, Ningbo 315100, Zhejiang,China

Keywords: Particle swarm optimization, aggregation degree, differential evolution, inertia weight, adaptive adjustment, PSO algorithm.

Abstract: Objective: In view of the issue that particle swarm optimization algorithm is easy to fall into local optimal solution and low convergence precision when searching for the feasible space solution of complex multimodal functions, the paper proposes an adaptive particle swarm optimization algorithm (PSO) based on aggregation degree.

Methods: The algorithm firstly makes a random disturbance to search global optimal solution through differential evolution method according to the size of population aggregation to increase the diversity of particle swarm and improve its global searching ability. Then the adaptive adjustment is made to the inertia weight parameter of particles in accordance with the aggregation degree of particles.

Conclusion: The simulation example of typical optimization problems suggests that this algorithm can improve the population diversity, particle activity, convergence performance and searching ability of PSO.

Cite this article as:

Zhang Xiuli *, Zhang Ruihua , Wang Jianping and Wang Laidi , An Adaptive Particle Swarm Optimization Algorithm Based on Aggregation Degree, Recent Advances in Electrical & Electronic Engineering 2018; 11 (4) . https://dx.doi.org/10.2174/2352096511666180214125027

DOI https://dx.doi.org/10.2174/2352096511666180214125027	Print ISSN 2352-0965
Publisher Name Bentham Science Publisher	Online ISSN 2352-0973