Imitation-based Cognitive Learning Optimizer for solving numerical and engineering optimization problems

Abstract

A novel human cognitive and social interaction-based metaheuristic called Imitation-based Cognitive Learning Optimizer (CLO) is proposed and developed to solve engineering optimization problems effectively. CLO is inspired by humans’ imitation and social learning behavior during the life cycle. The human life cycle consists of various stages. Social and imitating human behavior during the life cycle is incorporated into this algorithm to improve cognitive abilities. The three real-world mechanical engineering optimization problems (Welded beam problem, Tension–Compression String Design Problem, and Speed reducer problem) and 100 challenging benchmark functions including uni-modal, multi-modal and CEC-BC-2017 functions are used for the real-time validation. CLO is compared with 12 state-of-art algorithms from the literature. The experiments along with convergence analysis and Friedman’s Mean Rank (FMR) statistical test show the superiority of CLO over the other chosen algorithms.