Research On Hyper Heuristic Algorithm And Its Application In Low Carbon Location Routing Problem

Location routing problem(LRP)is a hot issue in the field of logistics.Under the environment of green logistics,it is of great scientific significance and practical value to study the modeling and optimization theory of carbon emission in logistics distribution location route based on the starting point of energy conservation and emission reduction.At present,there are many kinds of models about logistics problems,so it is necessary to have good generality for solving algorithms.The hyper heuristic algorithm is a new heuristic algorithm developed in recent years.It can be simply described as "heuristic algorithm seeking heuristic algorithm".It provides a high-level heuristic method to generate new heuristic algorithms by managing or manipulating a series of low level heuristic algorithms(Low-Level Heuristics,LLH).These new heuristics are applied to solve all kinds of combinatorial optimization problems.The metaheuristic algorithm has good universality,and does not need to set complex parameters for different instances,so that high quality solutions can be obtained.Therefore,it is of great significance to study the application of metaheuristic algorithm in LRP problem.This paper analyzes the types and specificity of the metaheuristic algorithm and applies it to the problem of low carbon LRP.Contains some specific work:1.In view of the current VRP and LRP problems,there are few studies on the structural operators.It is basically a random initial solution.A heuristic operator based on the structure is proposed,called the adaptive ant colony construction operator.The operator constructs the solution process by imitating the ant colony algorithm,and selects every ant's choice of the next city as a operation and provides the upper layer selection policy call.The experimental results show that under the upper selection strategy based on off-line learning,the operator library improves the quality of the initial solution and the convergence of the algorithm by adding the adaptive ant colony construction operator.2.In view of the problem that the upper selection strategy of the hyper heuristic algorithm is based on the problem that the single point search is easy to fall into the local optimal,the selection strategy based on the initial solution table is proposed.In this method,multiple initial solutions are introduced into the table to start iterative search at the same time,and then a single initial point is eventually locked by selecting the elimination.Experiments show that the algorithm can make the algorithm jump out of the local optimal solution and improve the quality of the solution by about 10% under the same time complexity.3.In order to discover the internal relations between the underlying heuristic operators and effectively search out the excellent combination of rules,this paper puts forward the upper selection strategy of the frog jump algorithm as a superheuristic algorithm.In the process of frog leaping algorithm,a similarity calculation method based on the longest common subsequence is proposed,and the similarity of individuals is determined by dynamic programming.Experiments show that the similarity computation method can better reflect the similarity between individuals and achieve higher quality solutions.Finally,this paper summarizes the whole paper,and points out the shortcomings of the existing hyperheuristic algorithm research,which provides a reference value for future algorithm research.