Lion Swarm Optimization Based On Improved Local Search Mechanism And Its Application

Compared with traditional mathematical methods,swarm intelligence optimization algorithm has good adaptability and strong global search ability because of its intelligence,parallelism and robustness.Because of its simplicity and high efficiency,it is widely used to solve practical problems.The swarm intelligence optimization algorithm achieves a balance between the solution time and the solution precision,and can solve the acceptable solution in an acceptable time.Swarm intelligence optimization algorithm has heuristic characteristics,some of which imitate the physiological structure and body function of organisms,some of which imitate the group behavior of animals,and some of which imitate the evolution process of the biological world.Lion swarm optimization is a new swarm intelligence optimization algorithm which imitates the behavior of lions.The basic swarm optimization is easy to fall into the local optimal solution in the process of partial optimization.In this paper,the local search mechanism of lion swarm optimization is improved by different methods.The main research contents and innovation points of this paper include the following aspects.Firstly,in the process of partial optimization of lion swarm optimization,the optimization efficiency is not very high because the optimization direction of young lions will deviate from the optimal direction of the lion pride with a certain probability.The fruit fly optimization algorithm adopts population-based random search strategy to guide the next search of the population by tracking the information of the current optimal solution,so that the population can carry out local random search with the current optimal solution as the center and search in a more optimal direction.By combining the basic lion swarm optimization with the visual search part of the fruit fly optimization algorithm,this paper presents a lion pride algorithm based on visual search.The local search capability of the lion swarm optimization is improved by updating it in the juvenile lion population with a certain probability.Secondly,to improve the global exploration capability of lion swarm optimization,this paper presented a novel lion swarm optimization based on multi-agent structure.The algorithm combined the effect of scale brought in the lion swarm optimization with the intelligence of the agent after information interaction and learning.The algorithm could use the group information and environment information to determine the search strategy in the search process.Simulation experiments show that the optimization accuracy of the improved algorithm is better than the basic algorithm.In solving the economic load distribution problem of power system,the lion swarm optimization based on multi-agent structure has significantly improved compared with lion swarm optimization.The experimental result on power system proves the effectiveness of the algorithm.Finally,in order to improve local search capability and convergence accuracy of the basic lion pride algorithm in the partial optimization process,a lion swarm optimization based on chaotic search and gaussian disturbance is proposed.The algorithm added chaotic search and gaussian disturbance to the location of the lion king,which improves the efficiency of the algorithm.The simulation results of the test function show that compared with the basic lion swarm optimization,the optimization accuracy of the improved algorithm is greatly improved,which effectively prevents the lion swarm optimization from falling into the local optimal value in the extremely difficult optimization function.Finally,a job shop scheduling example problem aiming at minimizing the total job processing time is tested,and the test results verify the effectiveness of the algorithm.