An improved sparrow search algorithm with multi-strategy integration.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Sparrow Search Algorithm (SSA) suffers from low convergence accuracy and a tendency to get trapped in local optima.
• Existing optimization algorithms require improvements in search capability, local optimum avoidance, and population diversity.

Purpose of the Study:

• To propose a novel Multi-strategy Integrated Sparrow Search Algorithm (MISSA) to overcome the limitations of the standard SSA.
• To enhance the global search ability, improve the capability to escape local optima, and increase population diversity in optimization.

Main Methods:

• Developed an Improved Search Strategy (ISS) by enhancing the black-winged kite algorithm for the producer's position update.
• Introduced a Group Follow Strategy (GFS), inspired by the Coot algorithm, to improve local optimum escape.
• Implemented a Random Opposition-Based Learning Strategy (ROBLS) post-iteration to boost population diversity.

Main Results:

• MISSA was extensively tested on 24 benchmark functions and CEC 2017 functions.
• Experimental results and Wilcoxon rank-sum tests confirmed MISSA's superior performance.
• MISSA outperformed the standard SSA and other advanced optimization algorithms.

Conclusions:

• The proposed MISSA effectively addresses the shortcomings of the SSA, demonstrating enhanced optimization capabilities.
• MISSA exhibits superior overall performance, making it a promising algorithm for complex optimization problems.