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Sine cosine particle swarm optimization algorithm for optimizing large scale issues.

Yao Wang1

  • 1Department of Science, Taiyuan Institute of Technology, Taiyuan, 030008, China. Wangxiaobian7908@126.com.

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Summary
This summary is machine-generated.

This study introduces a novel Sine Cosine Particle Swarm Optimization algorithm to address limitations in large-scale optimization problems. The enhanced algorithm demonstrates superior performance in benchmark tests and robot path planning, ensuring efficiency and accuracy.

Keywords:
Dynamic position correctionLarge-scale issuesPSOPath planningSine cosine algorithmSolve

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Area of Science:

  • Computational Intelligence
  • Robotics
  • Optimization Algorithms

Background:

  • Traditional hybrid algorithms struggle with local optima, diversity, and accuracy in large-scale problems.
  • Existing methods require enhancement for efficient and reliable problem-solving.

Purpose of the Study:

  • To develop an improved Sine Cosine Particle Swarm Optimization algorithm for large-scale optimization.
  • To enhance convergence accuracy, diversity, and efficiency in solving complex problems.
  • To validate the algorithm's effectiveness in robot path planning applications.

Main Methods:

  • An improved Sine Cosine Algorithm (SCA) was developed with dynamic position correction and an orthogonal crossover mechanism.
  • The enhanced SCA was combined with the Particle Swarm Optimization (PSO) algorithm, creating the Sine Cosine Particle Swarm Optimization (SCPSO) algorithm.
  • The SCPSO algorithm was tested on Shere and Quartic benchmark functions and applied to robot path planning.

Main Results:

  • SCPSO achieved an average and standard deviation of 0 on the Shere benchmark function.
  • On the Quartic benchmark function, SCPSO yielded an average of 3.48 × 10-5 and a standard deviation of 2.72 × 10-5.
  • In robot path planning, SCPSO resulted in zero collisions, 0.12 rad/m path smoothness, 2.45s average planning time, and 98.6% obstacle avoidance success rate.

Conclusions:

  • The dynamic position correction and orthogonal crossover mechanisms significantly improve convergence accuracy and optimal fitness acquisition.
  • SCPSO demonstrates superior search ability and solution accuracy compared to traditional methods.
  • The algorithm offers an efficient and reliable solution for large-scale optimization and robot path planning in complex environments.