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A modified Whale Optimization Algorithm for exploitation capability and stability enhancement.

Kumeshan Reddy1, Akshay K Saha1

  • 1Discipline of Electrical, Electronics & Computer Engineering, University of KwaZulu-Natal, 238 Mazisi Kunene Road, Durban, 4041, South Africa.

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Summary
This summary is machine-generated.

This study enhances the Whale Optimization Algorithm (WOA), a swarm-based metaheuristic optimization technique, to improve its convergence speed and exploitation capabilities for high-precision optimization problems.

Keywords:
AlgorithmsConvergenceExploitationOptimizationWhale optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Swarm-based Metaheuristic Optimization Techniques (MOT) are widely used due to their simplicity and performance.
  • The Whale Optimization Algorithm (WOA) shows promise but suffers from slow convergence and poor exploitation.
  • Existing modifications to WOA require further enhancement for demanding optimization tasks.

Purpose of the Study:

  • To propose an enhanced Whale Optimization Algorithm (WOA) with improved exploitation capability and stability.
  • To address the limitations of the conventional WOA, specifically its convergence rate and exploitation efficiency.
  • To develop a more robust and effective metaheuristic optimization technique.

Main Methods:

  • Introduced modifications to the position update equations of the conventional WOA.
  • Developed a modified algorithm structure for enhanced performance.
  • Compared the proposed algorithm against state-of-the-art MOT and recent WOA modifications.

Main Results:

  • The enhanced WOA achieved superior results on 7 out of 10 CEC 2019 benchmark functions.
  • Demonstrated significant superiority when applied to practical optimization problems.
  • Exhibited a notably improved convergence rate compared to other tested techniques.

Conclusions:

  • The proposed enhanced WOA significantly improves exploitation capability and stability.
  • The modified algorithm offers a superior alternative to existing metaheuristic optimization techniques.
  • This enhanced WOA is effective for both benchmark and real-world optimization challenges.