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Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization.

Kuan Zhang^1,2, Yirui He¹, Yuhang Wang³

¹College of Information Science and Technology, Northeastern University, Shenyang 110000, China.

Biomimetics (Basel, Switzerland)

|May 24, 2024

Summary

This summary is machine-generated.

The improved multi-strategy sand cat optimization algorithm (IMSCSO) enhances swarm intelligence by addressing diversity issues. This novel approach improves performance on complex optimization problems.

Keywords:

CEC 2017 fitness–distance balancing strategy metaheuristic algorithm non-exclusive learning search sand cat swarm optimization

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Area of Science:

Computational Intelligence
Swarm Intelligence
Optimization Algorithms

Background:

The sand cat swarm optimization algorithm (SCSO) offers robust optimization but suffers from limited population diversity.
This limitation leads to inefficiency in complex problems and a tendency towards local optima.

Purpose of the Study:

To introduce an improved multi-strategy sand cat optimization algorithm (IMSCSO).
To enhance the SCSO's convergence, diversity, and exploitation capabilities for complex optimization tasks.

Main Methods:

Incorporated a roulette fitness-distance balancing strategy for enhanced exploration.
Introduced a novel population perturbation strategy to escape local optima.
Developed a best-worst perturbation strategy to maintain diversity and improve exploitation.

Main Results:

Experiments on the CEC 2017 test suite demonstrated superior performance of IMSCSO.
The proposed IMSCSO outperformed seven other comparative algorithms.

Conclusions:

The IMSCSO effectively addresses the limitations of the original SCSO.
The multi-strategy enhancements result in significantly improved optimization performance, particularly for complex problems.