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FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion.

Zheng Zhang1, Xiangkun Wang2, Li Cao2

  • 1School of Information Engineering, Wenzhou Business College, Wenzhou 325035, China.

Biomimetics (Basel, Switzerland)
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Summary
This summary is machine-generated.

A new FOX optimization algorithm uses adaptive spiral flight and multi-strategy fusion to overcome limitations like local optima. This enhanced method shows improved convergence speed, accuracy, and stability in tests.

Keywords:
adaptive weightsfox optimization algorithmlevy flighttent chaotic mappingvariable spiral search strategy

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Original FOX optimization algorithm suffers from weak ergodicity, low diversity, and tendency to get stuck in local optima.
  • Enhancing swarm intelligence algorithms is crucial for solving complex optimization problems.
  • Initialization strategies significantly impact algorithm performance.

Purpose of the Study:

  • To develop an enhanced FOX optimization algorithm addressing the limitations of the original method.
  • To improve the exploration and exploitation capabilities of the FOX algorithm.
  • To evaluate the performance of the enhanced algorithm on benchmark functions and engineering problems.

Main Methods:

  • Initialization using a Tent chaotic map for improved ergodicity and diversity.
  • Integration of inertial weight to enhance solution quality.
  • Variable spiral position updating strategy for adaptive random walk.
  • Incorporation of Levy flight and greedy approach for balanced global and local search.
  • Fusion of multiple strategies for enhanced optimization.

Main Results:

  • The enhanced FOX algorithm demonstrated superior convergence speed compared to other swarm intelligence algorithms.
  • Significant improvements in solution accuracy and stability were observed.
  • The algorithm showed a greater ability to escape local optima.
  • Effective performance on CEC2017 benchmark test functions and engineering application optimization problems.

Conclusions:

  • The enhanced FOX optimization algorithm effectively overcomes the drawbacks of the original method.
  • The multi-strategy fusion approach leads to notable advancements in optimization performance.
  • The upgraded algorithm offers a robust and efficient solution for complex optimization tasks.