Research On The Teaching-learning-based Optimization Algorithms For Rescue Center Location Problem

In recent years,the frequency of sudden natural disasters or man-made accidents has been increasing,which makes people face serious life threats and economic losses.It is very important to choose a scientific and reasonable location to build an emergency rescue center.A reasonable location can make the surrounding area get assistance in the shortest time when a sudden disaster occurs,so as to minimize the travel time of rescue vehicles and minimize the further casualties caused by untimely rescue of the victims.However,the rescue center location problem(RCLP)involve complex scenarios,and it belongs to NP-Hard problem in the field of science and technology research.Using accurate algorithm to solve this kind of problems often can’t get the final result because of the complexity of the problems.It is efficient and effective for solving NP-Hard problem by using evolutionary algorithm.Therefore,evolutionary algorithm has become an effective method to solve rescue center location problem.In this paper,the subject of research on the teaching-learning-based optimization(TLBO)algorithms for rescue center location problem is proposed.TLBO algorithm is an evolutionary algorithm that simulates the teaching activities between teachers and students.Compared with other evolutionary algorithms,this algorithm has the advantages of simple evolution mechanism,fewer parameters and lower programming difficulty.It has been successfully applied in many fields.However,this algorithm is prone to premature phenomenon and the low precision of the solution.In this paper,TLBO algorithm is applied to solve rescue center location problem.Based on the solving results,this algorithm is improved and the rescue center location scheme is obtained.The main research work of this paper is introduced: 1.The characteristics of rescue center location problem are studied and analyzed.According to the demand of rescue vehicle travel time and construction cost in the rescue center location problem,a bi-level programming model is established.The model considers the impact of traffic congestion on the travel time of different roads in different time periods on the basis of satisfying the assumptions.2.Introduce the evolution mechanism and algorithm flow of the TLBO algorithm,design the coding scheme,use the TLBO algorithm to solve rescue centerlocation problem,and analyze the experimental results and algorithm performance.3.According to the solving effect of TLBO algorithm on the rescue center location problem,two improved schemes are designed and corresponding algorithms are proposed.Two improved algorithms are respectively applied to solve the rescue center location problem again.The experimental results show that the evolutionary strategies of the two improved algorithms can improve the accuracy,convergence speed and exploration ability of the algorithm.The innovations of this paper are as follows: 1.A TLBO with weighted center(WCTLBO)algorithm is proposed,which adjusts the evolution strategy of "teaching" and "learning" operators.WCTLBO algorithm changes the situation that a single teacher solve determines direction of population movement,so as to make full use of the population information.The center idea is fused with "learning" operator,several individuals are randomly selected to form a team,and the group center is selected with a certain probability to realize individual self-study.2.This paper proposes a TLBO with neighborhood structure mutation(NSMTLBO)algorithm.The mutation operator based on neighborhood structure is fused with TLBO algorithm,it makes each individual mutate by exchanging,reversing or inserting,so as to obtain a new individual.