Theory Evolution Optimization: A Metaheuristic Algorithm BaSed on Evolution Process of Theory

Abstract

Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods. To advance the field’s focus on algorithmic performance, this study introduces the Theory Evolution Optimization (TEO) – an efficient metaheuristic inspired by the evolution of scientific theory. TEO simulates the competitive, accumulative, and replacement processes among scientific hypotheses, mirroring the evolution from a hypothesis to an established scientific theory. The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms, including highly competitive champions such as EBOwithCMAR, LSHADE_cnEpSi, and LSHADE. Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test, with multiple comparisons managed by the Friedman test. Initially, TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions. TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications. Additionally, a binary TEO (BTEO) variant is introduced and applied to feature selection tasks in discrete space. Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems. The source codes for this research are accessible to the public at https://aliasgharheidari.com/TEO.html.