Tuckman Optimization Algorithm: A novel metaheuristic inspired by Tuckman’s Stages of Group Development for solving benchmark and engineering problems

Meifeng Shi; Faxiang Wang

doi:10.54097/2x049q44

Authors

Meifeng Shi
Faxiang Wang

DOI:

https://doi.org/10.54097/2x049q44

Keywords:

Tuckman’s Stages of Group Development, Meta-heuristic, Optimization Problems, Engineering Applications

Abstract

This paper proposes a novel Tuckman Optimization Algorithm (TOA) based on Tuckman’s Stages of Group Development for solving optimization and engineering design problems. TOA is the first to embed this theory into an intelligent optimization framework, simulating team dynamics to guide population search behavior. Its core innovations include a team control parameter and a team structure reconfiguration mechanism. The control parameter dynamically adjusts stage weights and evolutionary strategies based on iteration progress, enhancing adaptability and convergence stability. The team structure reconfiguration mechanism, based on a logistic growth model, replaces inferior individuals to maintain population vitality and avoid premature convergence. Experimental results on the 10-dimensional CEC2017, CEC2020, and CEC2022 benchmark suites show that TOA outperforms three baseline algorithms, ten advanced metaheuristics, and the CEC2017 winner LSHADE SPACMA in both accuracy and robustness. Additional tests on engineering design problems further validate TOA’s effectiveness and broad applicability in real-world optimization scenarios.

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