Lifecycle-Based Swarm Optimization Method for Numerical Optimization. (11th December 2014)

• Record Type: Journal Article
• Authors: Shen, Hai; Zhu, Yunlong; Liang, Xiaodan
• Other Names: Iqbal Muhammad Naveed Academic Editor.
• Is Part Of: Discrete dynamics in nature and society. Volume 2014(2014)
• Publication Date: 2014-12-11
• Subjects: System analysis -- Periodicals; Dynamics -- Periodicals; Chaotic behavior in systems -- Periodicals; Differentiable dynamical systems -- Periodicals; 003.05
• DOI: 10.1155/2014/892914
• Languages: English
• ISSNs: 1026-0226

Abstract

Abstract : Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.