Aphid Optimization Algorithm And Application Research

The swarm intelligent optimization algorithm is an optimization algorithm established by simulating the group behavior of organisms in nature,which can solve the problems of low solution accuracy and slow convergence in traditional optimization algorithms.Because of its strong self-adaptability and robustness,the swarm intelligence optimization algorithm can efficiently solve many practical problems.In this study,we propose a novel swarm intelligence optimization algorithm inspired by the foraging behavior of aphids,the aphid optimization algorithm.The main work of this study is as follows:(1)A single-objective aphid optimization algorithm is proposed.First,the biological and behavioral characteristics of aphids are analyzed,and a mathematical model is established based on their foraging behavior.In the generation stage of winged aphids,K-means clustering is used to simulate the population distribution? in the flight stage,the flight pattern is adaptively adjusted according to the crowded state to ensure the diversity of the algorithm? in the attack stage,the global optimal individual and individual optimal individual are used to guide the population evolution to the optimal solution to improve the convergence performance of the algorithm.Secondly,simulation experiments are conducted by benchmarking test functions to prove that the single-objective aphid optimization algorithm is highly competitive and solves the problems that the existing single-objective intelligent optimization algorithm is easy to fall into local optimum and the solution accuracy is not high.Finally,the singleobjective aphid optimization algorithm is applied to solve the welded beam design problem and the pressure vessel design problem to verify the feasibility of the algorithm in the constrained engineering optimization design problem.(2)A multi-objective aphid optimization algorithm is proposed.First,based on the single-objective aphid optimization algorithm,the multi-objective aphid optimization algorithm evolves iteratively in each class by the K-means clustering method? a method based on non-dominated sorting mechanism and special congestion distance is used for environment selection to ensure the diversity and convergence of the algorithm.Second,the algorithm performance is verified by simulation experiments with benchmark test functions.The experimental results show that the multi-objective aphid optimization algorithm can enhance the diversity of solution distribution in the decision space,improve the ability of the algorithm in global search,and solve the problem of uneven distribution of Pareto solution sets in multimodal optimization problems.Finally,the multi-objective aphid optimization algorithm is used to solve the nonlinear system of equations problem and compared with other algorithms to verify the effectiveness of the algorithm in solving multimodal problems.