Improved Egret Swarm Optimization Algorithm with Multiple Strategies

Abstract— The Egrets Swarm Optimization Algorithm is a recently proposed heuristic algorithm that simulates the hunting behavior of egrets. To address the limitations of the original algorithm, such as insufficient development capability and decreased population diversity, a multi-strategy improved Egrets Swarm Optimization Algorithm is proposed. First, in the population initialization phase, Logistic chaotic mapping is introduced to generate chaotic sequences, enriching population diversity. Next, a dynamic perception factor is introduced to replace the step size factor in the idle strategy, which allows for more effective exploration and discovery of potential optimal solutions. Furthermore, to increase the breadth and depth of exploration, a crayfish foraging strategy is incorporated into the random walk strategy, and a roulette wheel strategy is added to the encircling mechanism to enhance the algorithm’s search ability and avoid ineffective actions. Additionally, a distribution estimation strategy and a new exploration and exploitation strategy based on whale spiral ascent are introduced to improve the overall efficiency and functionality of the algorithm. Finally, testing on 20 classical functions shows that the improved algorithm enhances optimization performance. The algorithm is also applied to solve engineering constraint problems, demonstrating its practicality.