Improvement And Application Of Salp Swarm Algorithm And Harris Hawks Optimization Algorithm

Swarm intelligence optimization algorithm(SI)is an emerging meta-heuristic optimization technology,which has the advantages of simple principle,easy implementation,and strong flexibility.In recent years,many novel SI algorithms have appeared one after another,such as salp swarm algorithm(SSA),Harris hawks optimization(HHO),etc.As the complexity of real-world optimization problems increases,the newly proposed SI algorithm still has defects such as low optimization accuracy and slow convergence speed.Aiming at the shortcomings of SSA and HHO algorithms in solving complex optimization problems,this paper proposes a new improvement plan and successfully uses it to solve actual engineering optimization cases.The specific research content is as follows:(1)Aiming at the shortcoming of the SSA algorithm in high-dimensional optimization problems that it is easy to fall into local optimum,an improved salp swarm algorithm based on gravitational and multi-leader search strategies(GMLSSA)is proposed.In the gravitational search strategy,multiple salp individuals are used to guiding the location update of the search agent,which can get rid of the limitations of single individual guidance and improve the exploration ability of the algorithm.In the multi-leader strategy,the original population is divided into multiple independent sub-swarms to increase the diversity of the population and avoid falling into the local optimum.Simulation experiments on 20 benchmark functions and tension/compression spring design problems show that the proposed GMLSSA is significantly better than the original SSA algorithm and other comparison algorithms.(2)Aiming at the shortcomings of the insufficient exploration ability of the HHO algorithm,an improved Harris hawks optimization based on adaptive cooperative and dispersed foraging strategies(ADHHO)is proposed.In the adaptive cooperative foraging strategy,part of the eagle swarm is embedded in the one-dimensional update operation,and one-dimensional and full-dimensional cooperative foraging strategies are adaptively selected to increase the diversity of the population.In addition,through the decentralized foraging strategy,the eagles are scattered to different search spaces to find prey,making full use of the search space.Experiments on 20 benchmark functions and pressure vessel design problems have proved the significant advantages of the ADHHO algorithm.(3)In order to better balance the exploration and exploitation capabilities of the SSA algorithm,this paper proposes an improved salp swarm algorithm with Harris hawks foraging strategy(ISSAHF).The four foraging mechanisms in HHO are introduced into the follower location update process of SSA to strengthen the exploitation of potential areas,and multi-point leader cross strategy to achieve a balance between exploration and exploitation.Simulation experiments with 20 benchmark functions and tension/compression spring design problems show that the improved ISSAHF optimization accuracy is significantly better than the original SSA,ADHHO,GMLSSA and other comparison algorithms.