Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance
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, th...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97015-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850173012640268288 |
|---|---|
| author | Vipan Kusla Gurbinder Singh Brar Harpreet Kaur Ramandeep Sandhu Chander Prabha Md. Mehedi Hassan Shahab Abdulla Md Rittique Alam Samah Alshathri Walid El-Shafai |
| author_facet | Vipan Kusla Gurbinder Singh Brar Harpreet Kaur Ramandeep Sandhu Chander Prabha Md. Mehedi Hassan Shahab Abdulla Md Rittique Alam Samah Alshathri Walid El-Shafai |
| author_sort | Vipan Kusla |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-bd55da6c2f5e46c29ba7a5ff8959381d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bd55da6c2f5e46c29ba7a5ff8959381d2025-08-20T02:19:57ZengNature PortfolioScientific Reports2045-23222025-04-0115113510.1038/s41598-025-97015-1Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performanceVipan Kusla0Gurbinder Singh Brar1Harpreet Kaur2Ramandeep Sandhu3Chander Prabha4Md. Mehedi Hassan5Shahab Abdulla6Md Rittique Alam7Samah Alshathri8Walid El-Shafai9Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and TechnologySchool of Computer Science & Engineering, Lovely Professional UniversitySchool of Computer Science & Engineering, Lovely Professional UniversitySchool of Computer Science & Engineering, Lovely Professional UniversityChitkara University Institute of Engineering and Technology, Chitkara UniversityComputer Science and Engineering Discipline, Khulna UniversityUniSQ College, University of Southern QueenslandDepartment of Computer Science, American International University-BangladeshDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityAutomated Systems and Soft Computing Lab (ASSCL), Computer Science Department, Prince Sultan UniversityAbstract 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%.https://doi.org/10.1038/s41598-025-97015-1Morlet waveletLévy flightBenchmark functionsWireless sensor network (WSN)Cluster head |
| spellingShingle | Vipan Kusla Gurbinder Singh Brar Harpreet Kaur Ramandeep Sandhu Chander Prabha Md. Mehedi Hassan Shahab Abdulla Md Rittique Alam Samah Alshathri Walid El-Shafai Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance Scientific Reports Morlet wavelet Lévy flight Benchmark functions Wireless sensor network (WSN) Cluster head |
| title | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
| title_full | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
| title_fullStr | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
| title_full_unstemmed | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
| title_short | Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance |
| title_sort | chameleon swarm algorithm with morlet wavelet mutation for superior optimization performance |
| topic | Morlet wavelet Lévy flight Benchmark functions Wireless sensor network (WSN) Cluster head |
| url | https://doi.org/10.1038/s41598-025-97015-1 |
| work_keys_str_mv | AT vipankusla chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT gurbindersinghbrar chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT harpreetkaur chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT ramandeepsandhu chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT chanderprabha chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT mdmehedihassan chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT shahababdulla chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT mdrittiquealam chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT samahalshathri chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance AT walidelshafai chameleonswarmalgorithmwithmorletwaveletmutationforsuperioroptimizationperformance |