An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Meta-heuristics

Background:

• The Sand Cat Swarm Optimization (SCSO) algorithm, inspired by feline hunting behavior, faces challenges with local optima in later stages.
• Existing meta-heuristic algorithms require improvements in global search and convergence accuracy.

Purpose of the Study:

• To introduce an improved algorithm, LSSCSO, addressing the limitations of SCSO.
• To enhance the global search capability and convergence accuracy of the Sand Cat Swarm Optimization algorithm.

Main Methods:

• Developed LSSCSO by integrating dynamic spiral search, lens opposition-based learning, and the Sparrow Search Algorithm into SCSO.
• Validated LSSCSO performance using CEC2005 and CEC2022 test functions across various dimensions.
• Applied LSSCSO to engineering optimization problems to assess practical effectiveness.

Main Results:

• LSSCSO demonstrated superior optimization ability compared to standard SCSO.
• The algorithm effectively avoided local optima and improved convergence accuracy.
• Statistical analysis (Wilcoxon rank sum test) confirmed LSSCSO's robust performance.

Conclusions:

• LSSCSO significantly enhances the global search capability and convergence accuracy of the Sand Cat Swarm Optimization algorithm.
• The proposed LSSCSO algorithm shows strong potential for solving complex global and engineering optimization problems effectively.