Work

Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

Year: 2012

Type: article

Abstract: Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self‐adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

Source: Mathematical Problems in Engineering

DOI: 10.1155/2012/207318

Authors: Yu-Jun Zheng, Hai-Feng Ling, Qiu Guan

Institutions: Zhejiang University of Technology, PLA Army Engineering University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

Funder: National Natural Science Foundation of China