Multi-strategy Enhanced Salp Swarm Algorithm And Application Research

Salp Swarm Algorithm(SSA)is a new heuristic optimization algorithm proposed to simulate the foraging behavior of salp swarms in the sea.Due to its intuitive structure,easy to understand and strong search ability,SSA has been widely used in artificial intelligence,pattern recognition,system control,production scheduling and other fields.With the further study of SSA algorithm,we find that the algorithm is difficult to achieve high accuracy,low search efficiency and easy to fall into precociousness in the later stages of implementation.This paper analyzes and improves the shortcomings of the sea-sea optimization algorithm,and applies the improved algorithm to some complex optimization problems,with the aim of further perfecting the SSA algorithm theory,optimizing its performance and expanding its application range.The main work of this paper is as follows:(1)In order to enhance the population diversity of SSA algorithm and simulate the spiral topology of salp swarms foraging,a Polar Salp Swarm Algorithm is proposed,which can initialize the population and update the individual position in polar space,and update the individual position of salt by using the change of polar diameter and polar angle,thus increasing the population diversity of SSA algorithm and enhancing the search speed in the later period of the algorithm.The proposed Polar Salp Swarm Algorithm has achieved great success in engineering applications such as solving polar coordinate equations and optimizing spiral design.(2)In order to balance the exploration and development of Salp Swarm Algorithm,a golden sine and cosine optimization algorithm with variable neighborhood strategy is proposed.Its golden sine and cosine operator can balance the exploration and development of Salp Swarm Algorithm and improve the convergence speed and optimization accuracy.With the introduction of the variable neighborhood search strategy,the search range of the algorithm is expanded and the local optimum is avoided in the later period of the algorithm.Experimental simulation results show that the proposed golden sine and cosine Salp Swarm Algorithm with variable neighborhood strategy has better overall optimization performance.(3)Shape matching is the most representative method in the field of image recognition,in order to overcome the shortcomings of the traditional template matching algorithm with poor positioning and very large computation,the proposed gold sine sea otter optimization algorithm with variable neighborhood strategy is combined with the APM model to be applied to the shape matching problem,and compared with the original algorithm and a variety of new heuristic optimization algorithms,a large number of shape matching experimental results show that the gold cosine sea otter optimization algorithm with variable neighborhood strategy is better than other optimization algorithms.