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The improved osprey optimization algorithm (IOOA) enhances metaheuristic performance by integrating chaotic mapping, adaptive factors, and variation strategies. This novel approach boosts population diversity and avoids local optima for superior optimization accuracy.

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Algorithm Design

Background:

  • The Osprey Optimization Algorithm (OOA) is an effective metaheuristic but suffers from imbalanced exploration/exploitation and premature convergence.
  • Existing OOA limitations include susceptibility to local optima and reduced population diversity, hindering performance in complex optimization tasks.

Purpose of the Study:

  • To propose an improved Osprey Optimization Algorithm (IOOA) by incorporating multiple strategies to overcome the limitations of the original OOA.
  • To enhance the global exploration and local exploitation balance, increase population diversity, and improve convergence speed and accuracy.

Main Methods:

  • Initialization using Fuch chaotic mapping to increase initial population diversity.
  • Introduction of an adaptive weighting factor in the exploration phase to improve convergence accuracy.
  • Integration of the Cauchy variation strategy in the exploitation phase to avoid local optima and maintain population diversity.
  • Incorporation of a Warner mechanism from the Sparrow Search Algorithm to balance global and local search capabilities.

Main Results:

  • The IOOA demonstrated superior performance across 10 benchmark test functions and 15 CEC2017 test functions compared to other optimization algorithms.
  • Non-parametric tests confirmed the improved accuracy and stability of the IOOA.
  • The IOOA successfully applied to the three-bar truss engineering design problem, showcasing its effectiveness in practical engineering optimization.

Conclusions:

  • The proposed IOOA effectively addresses the limitations of the standard OOA, achieving enhanced optimization performance.
  • The multi-strategy fusion approach significantly improves population diversity, convergence accuracy, and the ability to escape local optima.
  • The IOOA shows strong potential for solving complex real-world optimization problems, particularly in engineering design.