Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis.

ABSTRACT: In recent years, opposition-based learning (OBL) has emerged as a powerful enhancement strategy in metaheuristic algorithms (MAs), gaining significant attention for its potential to accelerate convergence and improve solution quality. Existing research lacks a structured analysis of how different OBL variants influence optimization performance when integrated into various MAs. This study categorizes and analyzes nine distinct OBL techniques: basic opposition-based learning, quasi-opposition-based learning, generalized opposition-based learning, current optimum opposition-based learning, quasi-reflection opposition-based learning, centroid opposition-based learning, random opposition-based learning, super opposition-based learning, and stochastic opposition-based learning. To systematically

SUBMITTER: Lakbichi R 

PROVIDER: S-EPMC12453659 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature