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MSO: A Modified Snake Optimizer for Engineering Applications.

Hongxi Wang1, Likun Hu1

  • 1School of Electrical Engineering, Guangxi University, Nanning 530004, China.

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Summary

The Modified Snake Optimizer (MSO) enhances bio-inspired algorithms for complex engineering problems. It improves initialization, exploration, and exploitation, achieving superior performance and faster convergence.

Keywords:
RIMEUAV path planningdual mappingengineering optimization problemsopposition-based learningsnake optimizer

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Area of Science:

  • Engineering Optimization
  • Computational Intelligence
  • Bio-inspired Algorithms

Background:

  • Mathematical optimization is crucial for complex engineering challenges.
  • Bio-inspired metaheuristic algorithms offer effective solutions.
  • The original Snake Optimizer (SO) algorithm has limitations in initialization, global search, and convergence speed.

Purpose of the Study:

  • To introduce a Modified Snake Optimizer (MSO) to overcome the limitations of the original SO algorithm.
  • To enhance the performance of the Snake Optimizer for complex optimization tasks.
  • To improve convergence speed, global search capability, and robustness.

Main Methods:

  • Developed MSO by integrating a dual mapping strategy (Latin hypercube sampling and logistic mapping) for population initialization.
  • Incorporated an opposition-based learning mechanism with scaling factors for enhanced exploration.
  • Integrated the soft-rime search strategy from RIME optimization for improved exploitation.

Main Results:

  • MSO demonstrated faster convergence speed compared to nine other algorithms on the CEC2017 benchmark.
  • Validated MSO's effectiveness on engineering design problems (pressure vessel, spring, bearing) and UAV path planning.
  • Achieved stronger robustness and greater stability in optimization tasks.

Conclusions:

  • The Modified Snake Optimizer (MSO) effectively addresses the drawbacks of the original SO algorithm.
  • MSO extends biomimetic principles, offering superior performance for complex optimization problems.
  • The proposed enhancements significantly improve convergence, exploration, and exploitation capabilities.