Improved Swarm Intelligence Algorithm And Application For Multimodal Optimization Problems

Optimization problems exist widely in scientific research and practical engineering.As application scenarios become more complex,research on optimization problems becomes more urgent.The multimodal optimization problem is an important part of the optimization field.Its goal is to search for multiple optimal solutions or approximate optimal solutions in the solution space.In practice,it is necessary to provide decision makers with as many optimal solutions as possible,which can reduce the risks and losses caused by emergencies.How to efficiently solve multimodal optimization problems has attracted extensive attention from scholars at home and abroad.The swarm intelligence algorithm has a fast convergence speed.To solve the multimodal optimization problem,it is necessary to combine the algorithm with the niching strategy.According to different algorithms and strategies,this paper proposes an improved algorithm for solving continuous and discrete multimodal optimization problems,focusing on the effectiveness and limitations of the improved algorithm for solving multimodal optimization problems.The research contents mainly includes:(1)The improved squirrel search algorithm is used to deal with continuous multimodal optimization problems.First,the clustering algorithm is used to divide the population,and the sub-population evolves in parallel.Then,the neighborhood search strategy based on Gaussian distribution is used to improve the quality of the solution.Finally,the crowding strategy is adopted to retain the multiple global optimal solutions found during the operation of the algorithm.(2)The improved ant colony algorithm is used to deal with discrete multimodal optimization problems.Compared with the original algorithm,the improved algorithm uses multiple pheromone matrices to guide the ants in different areas of the search space,thereby slowing the convergence rate of the population.Since expanding the global search range weakens the algorithm's exploitation ability,the 2-opt strategy is used to randomly select a certain interval of the solution is optimized.In order to ensure the quality of the solution returned by the algorithm,the redundant solution in the algorithm is eliminated through the preprocessing operation.The proposed improved algorithm is compared with other multimodal optimization algorithms.Experimental results show that the proposed algorithm can obtain most of the global optimal solutions in the problem,and it has certain competitiveness compared with other algorithms.