ABSTRACTS

摘要

Recently, Particle Swarm Optimization (PSO) algorithm, which belongs to the broad class of stochastic optimization algorithms, has attracted a significant amount of interest from many researchers. PSO algorithm is a promising one to detect the minimum of a multimodal function with many local and global minima. However, it has been confirmed that the performance of PSO algorithm on large-scale optimization problems is not always satisfactory. This paper proposes a new optimization algorithm based on Artificial Bee Colony (ABC) algorithm that has the good performance on large-scale optimization problems. We evaluate the proposed algorithm through numerical experiments on large-scale benchmark problems and discuss its development potential. In numerical experiments, the performance of the proposed algorithm is compared with those of the existing swarm intelligence algorithms.