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Multi-Strategy Improved Whale Optimization Algorithm and Its Engineering Applications.

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The Improved Whale Optimization Algorithm (ImWOA) enhances global search and convergence speed. This novel algorithm addresses limitations of the original Whale Optimization Algorithm (WOA) for complex optimization tasks.

Keywords:
combined mutation mechanismdynamic elastic boundary optimization strategyimproved random searching strategyimproved whale optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Whale Optimization Algorithm (WOA) offers simplicity and good local optima avoidance but suffers from inefficient global search and slow convergence.
  • Existing WOA variants require improvements to address these inherent limitations in tackling complex optimization problems.

Purpose of the Study:

  • To introduce an Improved Whale Optimization Algorithm (ImWOA) that overcomes the global search and convergence speed limitations of the standard WOA.
  • To enhance the overall search capability and population diversity of the WOA through novel strategies.

Main Methods:

  • Implemented a dynamic elastic boundary optimization strategy to guide solutions within permissible limits.
  • Integrated an advanced random searching strategy balancing global and local exploration using optimal and mean positions.
  • Employed a combined mutation mechanism to increase population diversity and prevent premature convergence.

Main Results:

  • ImWOA demonstrated superior performance over five metaheuristic algorithms and three WOA variants on CEC2017 benchmark functions.
  • Achieved higher optimization accuracy, stability, and faster convergence speed across various dimensional scenarios (30D and 100D).
  • Successfully applied ImWOA to real-world engineering problems, including reducer design, vehicle side impact, and welded beam design.

Conclusions:

  • The proposed ImWOA effectively addresses the shortcomings of the standard WOA, offering improved global search and convergence.
  • ImWOA shows significant potential for application in diverse and complex engineering optimization domains.
  • The algorithm's robustness and efficiency are validated through extensive benchmark testing and real-world case studies.