A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Chandran, Vanisree; Mohapatra, Prabhujit

doi:10.1016/j.heliyon.2024.e30757

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e30757

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A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Vanisree Chandran,

Prabhujit Mohapatra

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Cited by 4 publications

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An improved tunicate swarm algorithm with random opposition based learning for global optimization problems

Chandran,

Mohapatra

2024

OPSEARCH

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An improved tunicate swarm algorithm with random opposition based learning for global optimization problems

Chandran,

Mohapatra

2024

OPSEARCH

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Optimization of stand-alone hybrid renewable energy system based on techno-socio-enviro-financial perspective using improved red-tailed hawk algorithm

Rathod,

2024

Applied Energy

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A novel reinforcement learning-inspired tunicate swarm algorithm for solving global optimization and engineering design problems

Chandran,

Mohapatra

2025

JIMO

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Reinforcement learning, specifically Q-learning, has gained a plethora of attention from researchers in recent decades due to its remarkable performance in various applications. This study proposes a novel Reinforcement Learning-inspired Tunicate Swarm Algorithm (RLTSA) that employs a Qlearning approach to enhance the convergence accuracy and local search efficacy of tunicates in TSA while preventing their local optimal entrapment. Firstly, a novel Chaotic Quasi Reflection Based Learning (CQRBL) strategy with ten chaotic maps is proposed to improve convergence reliability. Then, Q-learning is introduced and embedded with TSA by dynamically switching the learning mechanisms of CQRBL and ROBL strategies at different stages for distinct problems. These two strategies in the Q-learning approach significantly improve the efficiency of the proposed algorithm. The performance of RLTSA is evaluated on a set of 33 distinct functions, including the CEC'05 and CEC'19 test functions, as well as four engineering design problems, and its outcomes are statistically and graphically tested against the TSA and seven other eminent meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and t-tests, have been employed to exemplify the dominance of the RLTSA. The experimental findings disclose that RLTSA outperforms the competing algorithms in the realm of real-world engineering design problems.

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A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Cited by 4 publications

References 100 publications

An improved tunicate swarm algorithm with random opposition based learning for global optimization problems

An improved tunicate swarm algorithm with random opposition based learning for global optimization problems

Optimization of stand-alone hybrid renewable energy system based on techno-socio-enviro-financial perspective using improved red-tailed hawk algorithm

A novel reinforcement learning-inspired tunicate swarm algorithm for solving global optimization and engineering design problems

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