A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications
Vanisree Chandran, Prabhujit Mohapatra
TL;DR
This paper introduces an improved version of the Tunicate Swarm Algorithm to solve complex optimization problems more efficiently.
Contribution
The novel QOCTSA algorithm combines quasi-oppositional learning and chaotic local search to enhance optimization performance.
Findings
QOCTSA outperforms TSA in convergence accuracy and speed on benchmark test functions.
The algorithm shows superior performance in solving real-world engineering design problems.
Statistical tests confirm QOCTSA's effectiveness compared to other competing algorithms.
Abstract
Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering applications. As a result, this study attempts to boost the efficiency of the recently introduced bio-inspired algorithm, the Tunicate Swarm Algorithm (TSA), which is motivated by the foraging and swarming behaviour of bioluminescent tunicates residing in the deep sea. Like other algorithms, the TSA has certain limitations, including getting trapped in the local optimal values and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. To overcome these shortcomings, a novel multi-strategy ameliorated TSA,…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Scientific and Engineering Research Topics · Evolutionary Algorithms and Applications