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Related Experiment Video

Updated: Sep 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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GWOA: A multi-strategy enhanced whale optimization algorithm for engineering design optimization.

Yanzhao Gu1, Junhao Wei1, Zikun Li2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Plos One
|September 3, 2025
PubMed
Summary
This summary is machine-generated.

The improved Whale Optimization Algorithm (GWOA) enhances global search and convergence for complex problems. GWOA demonstrates superior performance in benchmark and engineering optimization tasks, offering a more stable and efficient solution.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Traditional Whale Optimization Algorithm (WOA) suffers from local optima, slow convergence, and limited global search for high-dimensional problems.
  • Existing WOA variants may not fully address these limitations in complex optimization scenarios.

Purpose of the Study:

  • To propose an improved Whale Optimization Algorithm (GWOA) addressing the shortcomings of the traditional WOA.
  • To enhance global search ability, convergence speed, and solution accuracy for complex optimization problems.

Main Methods:

  • Integration of adaptive parameter adjustment, enhanced prey encircling, and sine-cosine search strategies into WOA.
  • Testing GWOA on 23 benchmark functions (unimodal, multimodal, compositional) and engineering design problems (pressure vessel, spring).
  • Comparative analysis with basic metaheuristics, WOA variants, and state-of-the-art algorithms, including scalability experiments.

Main Results:

  • GWOA demonstrated superior convergence speed and solution accuracy across most benchmark functions, particularly for multimodal and compositional problems.
  • Achieved an Overall Efficiency (OE) of 74.46% in benchmark tests.
  • Effectively reduced costs and met constraints in engineering optimization problems, showing enhanced stability and optimization ability.

Conclusions:

  • GWOA significantly improves global search, convergence speed, and solution stability through multi-strategy integration.
  • GWOA shows great potential for solving complex optimization problems and serves as an efficient tool for engineering applications.
  • The proposed GWOA offers a robust and effective alternative to traditional optimization algorithms.