Thinking Innovation Strategy (TIS): A novel mechanism for metaheuristic algorithm design and evolutionary update

The metaheuristic optimization algorithm(MHS) is a global optimization method inspired by natural phenomena, demonstrating superior performance in specific application scenarios. Traditional optimization algorithms utilize two main concepts: exploration, to expand the search range, and exploitation,...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Applied soft computing Ročník 175; s. 113071
Hlavní autoři:	Jia, Heming, Zhou, Xuelian, Zhang, Jinrui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.05.2025
Témata:	Constrained engineering design problems Intelligent balanced exploration-exploitation Metaheuristic optimization algorithms Thinking transformation strategy Metaheuristic optimization algorithms Intelligent balanced exploration-exploitation Thinking transformation strategy Constrained engineering design problems
ISSN:	1568-4946
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	The metaheuristic optimization algorithm(MHS) is a global optimization method inspired by natural phenomena, demonstrating superior performance in specific application scenarios. Traditional optimization algorithms utilize two main concepts: exploration, to expand the search range, and exploitation, to enhance solution accuracy. However, as problem complexity and application scenarios increase, MHS struggles to balance exploration and exploitation to find the optimal solution. Therefore, this paper introduces innovative characteristics of individual thinking and proposes a new Thinking Innovation Strategy (TIS). TIS does not aim for an optimal solution but seeks global optimization based on successful individuals, enhancing algorithm performance through survival of the fittest. This paper applies TIS strategies to improve various MHS algorithms and evaluates their performance on 57 engineering problems and the IEEE CEC2020 benchmarks. Experimental results indicate that the TIS-enhanced algorithms outperform the original versions across 57 engineering problems, according to Friedman ranking and Wilcoxon rank-sum test results. Some algorithms show significant improvement, demonstrating the feasibility and practicality of TIS for optimization problems. The TIS (LSHADE_SPACMA) of the source code can be accessed through the following ways: https://github.com/LIANLIAN-Serendipity/TIS- [Display omitted] •This paper proposes an update strategy called Thinking Innovation Strategy.•TIS builds a complete mental innovation model.•TIS can be applied to most metaheuristic optimization algorithms.•The algorithm optimized by TIS has better optimization performance.
ISSN:	1568-4946
DOI:	10.1016/j.asoc.2025.113071