Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance
Article
Article Title | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | Kusla, Vipan, Brar, Gurbinder Singh, Kaur, Harpreet, Sandhu, Ramandeep, Prabha, Chander, Bhuiyan, Md. Mehedi, Abdulla, Shahab, Alam, Md Rittique, Abdulla, Samah, Samah, A. and El-Shafai, Walid |
Journal Title | Scientific Reports |
Journal Citation | 15 |
Article Number | 13971 |
Number of Pages | 35 |
Year | 2025 |
Publisher | Nature Publishing Group |
Place of Publication | United Kingdom |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-025-97015-1 |
Web Address (URL) | https://www.nature.com/articles/s41598-025-97015-1 |
Abstract | Metaheuristic algorithms play a vital role in addressing a wide range of real-world problems by overcoming hardware and computational constraints. The Chameleon Swarm Algorithm (CSA) is a modern metaheuristic algorithm that uses how chameleons act. To improve the capabilities of the CSA, this work proposes a modified version of the Chameleon Swarm Algorithm to find better optimal solutions applicable to various application areas. The effectiveness of the proposed algorithm is assessed using 97 typical benchmark functions and three real-world engineering design problems. To validate the efficacy of the proposed algorithm, it has been compared to a number of well-known and widely-used classical algorithms, the Gravitational Search Algorithm, the Earthworm Optimization. The proposed modified Chameleon Swarm Algorithm using Morlet wavelet mutation and Lévy flight (mCSAMWL) is superior to existing algorithms for both unimodal and multimodal functions, as demonstrated by Friedman’s mean rank test as well as three real world engineering design problems. Five performance metrics—average energy consumption, total energy consumption, total residual energy, dead node and cluster head frequency are taken into consideration when evaluating the performances against state-of-the-art algorithms. For nine different simulation scenarios, the proposed algorithm mCSAMWL outperforms the Atom Search Optimization (ASO), Hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSO-GWO), Bald Eagle Search Algorithm (BES), the African Vulture Optimization Algorithm (AVOA), and the Chameleon Swarm Algorithm (CSA) in terms of average energy consumption and total energy consumption by 50.9%, 52.6%, 45%, 42.4%, 50.1% and 51.4%, 53.3%, 45.6%, 42.4%, 50.7%. |
Keywords | Morlet wavelet; Cluster head; Wireless sensor network (WSN); Benchmark functions; Lévy flight |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 409999. Other engineering not elsewhere classified |
Byline Affiliations | Sant Longowal Institute of Engineering and Technology, India |
Lovely Professional University, India | |
Chitkara University, India | |
Khulna University, Bangladesh | |
Princess Nourah bint Abdulrahman University, Egypt | |
American International University, Bangladesh | |
Menoufia University, Egypt | |
Prince Sultan University, Saudi Arabia |
https://research.usq.edu.au/item/zx3qz/chameleon-swarm-algorithm-with-morlet-wavelet-mutation-for-superior-optimization-performance
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