Chandran Vanisree, Mohapatra Prabhujit
Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Heliyon. 2024 May 9;10(10):e30757. doi: 10.1016/j.heliyon.2024.e30757. eCollection 2024 May 30.
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, termed the Quasi-Oppositional Chaotic TSA (QOCTSA), has been proposed as an enhanced variant of TSA. This enhanced method contributes the simultaneous incorporation of the Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) mechanisms to effectively balance exploration and exploitation. The implementation of QOBL improves convergence accuracy and exploration rate, while the inclusion of a CLS strategy with ten chaotic maps improves exploitation by enhancing local search ability around the most prospective regions. Thus, the QOCTSA significantly enhances convergence accuracy while maintaining TSA diversification. The experimentations are conducted on a set of thirty-three diverse functions: CEC2005 and CEC2019 test functions, as well as several real-world engineering problems. The statistical and graphical outcomes indicate that QOCTSA is superior to TSA and exhibits a faster rate of convergence. Furthermore, the statistical tests, specifically the Wilcoxon rank-sum test and -test, reveal that the QOCTSA method outperforms the other competing algorithms in the domain of real-world engineering design problems.
在过去几十年里，人们提出了许多著名的元启发式算法来解决复杂的优化问题。然而，迫切需要通过采用各种进化技术来增强这些现有的元启发式算法，以应对工程应用中出现的挑战。因此，本研究试图提高最近引入的受生物启发的算法——海鞘群算法（TSA）的效率，该算法的灵感来自于生活在深海的发光海鞘的觅食和群体行为。与其他算法一样，TSA存在一定的局限性，包括陷入局部最优值和缺乏探索能力，导致在处理极具挑战性的优化问题时出现早熟收敛。为了克服这些缺点，一种新颖的多策略改进TSA，即准对立混沌TSA（QOCTSA），被提出作为TSA的增强变体。这种增强方法同时引入了基于准对立学习（QOBL）和混沌局部搜索（CLS）机制，以有效地平衡探索和利用。QOBL的实施提高了收敛精度和探索率，而包含具有十种混沌映射的CLS策略通过增强最有前景区域周围的局部搜索能力提高了利用能力。因此，QOCTSA在保持TSA多样性的同时显著提高了收敛精度。实验是在一组33个不同的函数上进行的：CEC2005和CEC2019测试函数，以及几个实际工程问题。统计和图形结果表明，QOCTSA优于TSA，并且收敛速度更快。此外，统计测试，特别是威尔科克森秩和检验和t检验，表明QOCTSA方法在实际工程设计问题领域优于其他竞争算法。
