The Improvement Of Mayfly Optimization Algorithm And Its Application In Optimization Problems

Mayfly optimization algorithm(MOA)is a new meta-heuristic optimization algorithm based on population,which was proposed by Konstantinos and others in2020.Its optimization process simulates the flight behavior and mating process of Mayfly population.Because of its clear structure and strong search ability,the algorithm has made great progress in some optimization problems.However,with the deepening of related research,the algorithm has some defects,such as easy to fall into local extremum in the later stage of search,unbalanced development and exploration capabilities and so on.Therefore,this paper analyzes and improves the Mayfly optimization algorithm,and puts forward three kinds of Mayfly optimization algorithms with different improvement strategies,which are successfully applied to practical optimization problems,further improving the optimization performance and expanding the application scope of the Mayfly optimization algorithm.The main research work of this paper is as follows:(1)In order to solve the problem of roundness error evaluation,an improved Mayfly optimization algorithm(IMOA)is proposed.Cauchy mutation operator and nonlinear inertia weight strategy are introduced on the basis of MOA to avoid the algorithm falling into local optimum and improve the optimization speed of the algorithm.In addition,the improved ephemera optimization algorithm is applied to four roundness error evaluation problems by combining the minimum region circle method and the least square method.The experimental results show that IMOA has obvious advantages in solving speed and accuracy.(2)Aiming at the shortage of searching ability of the basic Mayfly optimization algorithm,a Mayfly Optimization Algorithm based on Piecewise Mapping and Dynamic Weight(PDMOA)is proposed.The initialization strategy of Piecewise mapping and the dynamic inertia weight factor based on Sigmoid function are introduced into MOA,which enhances the population diversity and the possibility of jumping out of local optimization,and better balances the global search and local search capabilities of the algorithm.Finally,it is used to solve 23 benchmark functions and two engineering optimization problems.The experimental results show that the optimization performance of PDMOA is better than other comparison algorithms.(3)Aiming at the problem of low efficiency in solving chemical dynamic optimization problems,a multi-strategy Mayfly optimization algorithm(MMOA)is proposed.Combining MOA with chaos crossover factor,center wandering strategy and boundary domain correction strategy,the global search ability,search speed and population diversity are improved.Finally,it is applied to five classic chemical dynamic optimization problems by combining the control vector parameterization method.The results show that the performance index obtained by MMOA is still competitive compared with the data provided in the literature.