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Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance.

Vipan Kusla1, Gurbinder Singh Brar2, Harpreet Kaur3

  • 1Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, 148106, India.

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|April 22, 2025
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Summary
This summary is machine-generated.

A modified Chameleon Swarm Algorithm (CSA) with Morlet wavelet mutation and Lévy flight (mCSAMWL) enhances optimization for complex problems. This metaheuristic approach improves energy efficiency in simulations compared to existing algorithms.

Keywords:
Benchmark functionsCluster headLévy flightMorlet waveletWireless sensor network (WSN)

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Metaheuristic algorithms are crucial for solving real-world problems constrained by hardware and computation.
  • The Chameleon Swarm Algorithm (CSA) is a recent metaheuristic inspired by chameleon behavior.
  • Existing algorithms face limitations in finding optimal solutions for complex computational tasks.

Purpose of the Study:

  • To enhance the capabilities of the Chameleon Swarm Algorithm (CSA).
  • To develop a modified CSA (mCSAMWL) incorporating Morlet wavelet mutation and Lévy flight.
  • To evaluate the proposed algorithm's performance on benchmark functions and real-world engineering problems.

Main Methods:

  • The study proposes a modified Chameleon Swarm Algorithm (mCSAMWL) integrating Morlet wavelet mutation and Lévy flight.
  • The algorithm's effectiveness was tested on 97 benchmark functions and three engineering design problems.
  • Performance was compared against established algorithms like Gravitational Search Algorithm and Earthworm Optimization.

Main Results:

  • The modified Chameleon Swarm Algorithm using Morlet wavelet mutation and Lévy flight (mCSAMWL) demonstrated superiority over existing algorithms on unimodal and multimodal functions.
  • Friedman's mean rank test confirmed the proposed algorithm's effectiveness.
  • In simulations, mCSAMWL significantly reduced average and total energy consumption compared to ASO, PSO-GWO, BES, AVOA, and CSA.

Conclusions:

  • The modified Chameleon Swarm Algorithm (mCSAMWL) offers superior optimization capabilities compared to existing metaheuristic algorithms.
  • The proposed mCSAMWL effectively addresses computational and hardware constraints in optimization tasks.
  • This enhanced algorithm shows significant promise for various application areas, particularly in improving energy efficiency.