Improved sparrow search algorithm based on hybrid-strategy

• Main Authors: Liu, Xuehu, Liu, Zuhan
• Format: Conference Proceeding
• Language: English
• Published: SPIE 08.04.2024
• ISBN: 9781510675032; 1510675035
• ISSN: 0277-786X
• DOI: 10.1117/12.3025654

An advanced multi-strategy hybrid algorithm, denoted as the Butterfly-Inspired Hybrid Sparrow Search Algorithm (BFHSSA), has been developed to address issues related to slow convergence, local optimization, and inadequate precision within the iterative framework of the conventional sparrow search algorithm. The algorithm initiates the positions of individual sparrows using circular chaotic mapping, thereby augmenting the initial diversity and ergodicity of the population. Leveraging the perceptive motion mechanism of the Butterfly Optimization Algorithm (BOA), adjustments are made to the position update formula of the discoverer, resulting in an enhanced global optimization capability for the algorithm. Additionally, a sinusoidal search strategy is introduced to dynamically regulate the convergence speed of individual followers, thereby augmenting the algorithm’s capability to escape local optima. To assess the efficacy of the proposed algorithm, six fundamental test functions are chosen for rigorous simulation experiments. Comparative evaluations are conducted against prominent optimization algorithms, including the Particle Swarm Optimization Algorithm, Gray Wolf Algorithm, Whale Optimization Algorithm, and the original Sparrow Search Algorithm. The experimental outcomes unequivocally demonstrate that the BFHSSA exhibits superior convergence speed, heightened precision, and significantly improved optimization performance.