Enhanced Salp Swarm Algorithm And Application Research

The Salp Swarm Algorithm(SSA)is a swarm intelligence algorithm that has emerged in recent years.The Salp Swarm Algorithm inspired by the predatory and navigational behavior of marine animals such as Salp Swarm,and it is widely used in various fields.SSA has the characteristics of simple structure,easy implementation and good detection performance.However,at the later stage of iteration,SSA algorithm has some shortcomings,such as difficult to improve convergence accuracy,poor search ability and easy to fall into premature.This paper analyzes and improves some shortcomings of the salp swarm algorithm,proposes three improved SSA algorithms,and applies the improved algorithms to practical problems in order to further improve the theory of SSA algorithm and expand its application scope.The main work of this paper is as follows:(1)In order to balance the exploratory and exploitative nature of the algorithm,an improved algorithm based on hybrid mechanism and adaptive weighting factor is proposed.When initializing the population,the cubic chaos mapping and refraction reversal mechanism are introduced to improve the population diversity;The introduction of Levy flight mechanism promotes the generation of random step size in the leader position update,expands the search range,and avoids the algorithm from entering a local area and being unable to exit again;An adaptive weighting factor is introduced to balance the development and exploration performance of the algorithm.The improved algorithm based on hybrid mechanism and adaptive weighting factor has achieved certain results in dynamic optimization problems of chemical engineering.(2)This paper proposes an adaptive and improved algorithm based on disturbance factor.First,in the leader position update stage,disturbance factors are added to expand the search scope to improve the local search ability and guide individuals to explore other locations to increase the diversity of the population;Use the optimal position of the previous generation to replace the position of the previous generation to improve the position update of the follower to solve the problem of blind follower,and further strengthen the local search ability of the algorithm;In the improved follower position update stage,the inertia weight controlled by the negative hyperbolic tangent function is introduced to balance the global search and local search capabilities of the algorithm.The CEC2017 standard test set confirms the superiority of the new algorithm.The new algorithm is applied to the path planning problem,and the experimental results show that the algorithm is better than other heuristic algorithms.(3)In order to improve the precision and speed of SSA optimization,and avoid SSA from jumping out of local optimization,an improved salp swarm algorithm based on elite pool strategy and inertia weight is proposed.When initializing the population,Kent chaotic map is introduced to improve the diversity of the population;Introduction of elite pool strategy to expand search scope;The inertia weight is added to balance the algorithm development and exploration.The improved algorithm and other heuristic optimization algorithms are compared and analyzed.The experimental results show that the improved algorithm has good stability,reliability and has strong optimization performance in solving engineering cases.