PMSOMA: optical microscope algorithm based on piecewise linear chaotic mapping and sparse adaptive exploration.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The optical microscope algorithm (OMA) is a metaheuristic optimization technique inspired by optical microscopes.
• The original OMA suffers from slow convergence and susceptibility to local optima, limiting its practical application.
• Enhancing metaheuristic algorithms is crucial for solving complex optimization problems efficiently.

Purpose of the Study:

• To introduce an improved version of the OMA, named PMSOMA.
• To address the limitations of slow convergence and local optima in the original OMA.
• To enhance the diversity and search efficacy of the OMA using novel mechanisms.

Main Methods:

• Developed PMSOMA by integrating a piecewise linear chaotic map for population initialization and diversity enhancement.
• Incorporated a sparse adaptive exploration mechanism to improve search efficiency.
• Evaluated PMSOMA's performance on 50 benchmark functions, the CEC2017 test suite, feature selection datasets, and engineering challenges.

Main Results:

• PMSOMA demonstrated superior performance compared to the original OMA and other competing algorithms.
• The enhanced algorithm achieved faster convergence rates.
• PMSOMA exhibited improved robustness in finding optimal solutions across various test scenarios.

Conclusions:

• PMSOMA effectively overcomes the limitations of the original OMA.
• The proposed enhancements lead to more efficient and robust optimization.
• PMSOMA represents a significant advancement in metaheuristic algorithm design for complex problems.