Elite Opposition-Based Cognitive Behavior Optimization Algorithm for Global Optimization

Abstract

This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature.

Keywords

• cognitive behavior optimization
• elite opposition-based learning
• benchmark functions
• engineering design problems
• swarm intelligence