Harmony Search Algorithm And Its Application In Vehicle Routing Problem

Optimization problems are widespread in every field of human life,so it is more and more important to study efficient optimization methods.Swarm intelligent algorithm refers to that researchers construct relevant iterative mechanisms by simulating some phenomena and laws in nature,compared with traditional optimization algorithms,swarm intelligent algorithm has unique advantages in solving complex optimization problems.Harmony search algorithm is a new swarm intelligence algorithm which simulates the process of music creation.Its concept is simple,parameters need to be adjusted less,iteration mechanism is simple,easy to implement,and it has certain advantages in solving high dimensional and complex optimization problems.In order to further improve the optimization performance of the harmony search algorithm,this thesis proposes two ways to improve it and applies them to solve the vehicle routing problem.The main research work of this thesis is as follows:1.The harmony search algorithm based on chaos opposition-based learning and Cauchy mutation is proposed,and the proposed algorithm is used to solve the vehicle routing problem.In order to improve the quality of the initial population,chaos opposition-based learning strategy is used to initialize the harmony memory at the initialization stage of the harmony search algorithm.The individuals with better fitness value is selected as the initial solutions of the harmony memory from the initial solutions generated by chaotic mapping and its reverse solutions.In terms of parameter improvement,pitch adjusting rate and bandwidth are replaced by fixed values with dynamic parameters that change with the number of iterations.The Cauchy mutation strategy is introduced in the process of generating new solutions.When the random number satisfies the harmony memory considering rate,the new harmony is selected in the harmony memory,and then Cauchy mutation operation is performed on the selected harmony.When the random number meets the pitch adjusting rate,the tonal fine-tuning is performed on the basis of the Cauchy mutation,which improves the global exploration performance of the algorithm.Finally,the improved algorithm is used to simulate the benchmark test function and the vehicle routing problem,and the comparison algorithm is compared.The results show that the improved algorithm has the highest accuracy,and the stability of the results is stronger.2.The harmony search algorithm based on golden sine and cosine and lens imaging learning is proposed,and the proposed algorithm is used to solve the vehicle routing problem with time window.The golden sine and cosine strategy is introduced in the process of generating new solutions so that the harmony can fully explore the best search area.In order to avoid the algorithm falling into local optimization,lens imaging learning strategy is introduced in the late stage of the algorithm.Finally,the improved algorithm is used to simulate the benchmark test function and the vehicle routing problem with time window,and the comparison algorithm is compared.The results show that the improved algorithm has the best performance and good robustness.