Tunning parameters of evolutionary algorithm in Travelling Salesman Problem with profits and returns (original) (raw)

Evolutionary algorithm to traveling salesman problems

Computers & Mathematics with Applications, 2012

This paper proposed an improved version of the Particle Swarm Optimization (PSO) approach to solve Traveling Salesman Problems (TSP). This evolutionary algorithm includes two phases. The first phase includes Fuzzy C-Means clustering, a rule-based route permutation, a random swap strategy and a cluster merge procedure. This approach firstly generates an initial non-crossing route, such that the TSP can be solved more efficiently by the proposed PSO algorithm. The use of sub-cluster also reduces the complexity and achieves better performance for problems with a large number of cities. The proposed Genetic-based PSO procedure is then applied to solve the TSP with better efficiency in the second phase. The proposed Genetic-based PSO procedure is applied to TSPs with better efficiency. Fixed runtime performance was used to demonstrate the efficiency of the proposed algorithm for the cases with a large number of cities.

The Application of Genetic Algorithm in Solving Traveling Salesman Problem

2020

Traveling Salesman Problem (TSP) is one form of optimization problem with easy concept, but complicated if solved conventionally. The purpose of TSP is to build an optimal routes, with the rules of each city to be visited by salesmen and the cities are visited only exactly once, the trip begins and ends in the city early. To build the optimal routes, in this study using genetic algorithm. In the example case there are 4 cities that must be traversed by the salesman, that city A, B, C, and D with the trip starts from the city A and ends in city A as well. So obtained the optimal route that is [A D B C] with minimum distance that is 19 km.

A Fast Evolutionary Algorithm for Traveling Salesman Problem

2007

In this paper we proposed a new algorithm based on Inver-over operator, for traveling salesman problems (TSP). Inver-over is based on simple inversion; however, knowledge taken from other individuals in the population influences its action. In the new algorithm we use some new strategies including selection operator, replace operator and some new control strategy, which have been proved to be very efficient to accelerate the converge speed. We also use this approach to solve dynamic TSP. A dynamic TSP is harder than a general TSP, which is a NP-hard problem, because the city number and the cost matrix of a dynamic TSP are time varying, the algorithm to solve the dynamic TSP problem, which is the hybrid of EN and Inver-Over algorithm. Through the experiment, the new algorithm shows great efficiency in solving the static TSP and dynamic TSP.

IJMIE Study of Traveling Salesman Problem Using Genetic Algorithm

The traveling salesman problem is a permutation problem in which the goal is to find the shortest path between N different cities that the salesman takes is called the TOUR. In other words, the problem deals with finding a route covering all cities so that the total distance traveled is minimal. This paper gives a solution to find an optimum route for traveling salesman problem using Genetic algorithm technique, in which cities are selected randomly as initial population. The new generations are then created repeatedly until the proper path is reached upon reaching the stopping criteria.

A Hybrid Genetic Algorithm for the Travelling Salesman Problem

Studies in Computational Intelligence, 2008

Genetic Algorithms (GAs) for the Travelling Salesman Problem (TSP) are often based on permutation representations, which makes it difficult to design effective evolutionary operators without causing feasibility problems to chromosomes. This paper attempts to develop a binary representation based hybrid GA to solve the TSP. The basic idea is to design a pre-TSP problem (PTSPP), where the input is the coordinates of a point in the map of cities, and the output is a feasible route connecting all cities. An effective deterministic algorithm is developed for this PTSPP to search the local optimum starting from the coordinates of a given point. The new GA is then designed to randomly choose and evolve the coordinates of generations of points for the PTSPP, and also to find out the global optimum or suboptima for the TSP. The preliminary experiments show the potential of the proposed hybrid GA to solve the TSP.

A hybrid heuristic for the traveling salesman problem

IEEE Transactions on Evolutionary Computation, 2001

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. Local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13 509 cities show the efficacy of the symbiosis between the two heuristics.

A Novel Metaheuristic for Travelling Salesman Problem

One of the well-known combinatorial optimization problems is travelling salesman problem (TSP). This problem is in the fields of logistics, transportation, and distribution. TSP is among the NP-hard problems, and many different metaheuristics are used to solve this problem in an acceptable time especially when the number of cities is high. In this paper, a new meta-heuristic is proposed to solve TSP which is based on new insight into network routing problems.

EFFICIENT TECHNIQUE TO SOLVE TRAVELLING SALESMAN PROBLEM USING GENETIC ALGORITHM

In this paper, an efficient technique is proposed to solve the travelling salesman problem (TSP) using genetic algorithm. We can apply easily genetic algorithm operator to this problem and get the solution easily. Complexity is both in time and space, provided size of the problem an as integer. The solution of the traveling salesman problem is global optimum. There are number of cities and distances are given between them (cities). A Traveling salesman has to visit all of them. The salesman will start from city and after traversing the remaining cities (exactly once) he will reach to his starting position. Main objective of TSP is to find traveling sequence of cities to minimize the traveling distance so that the salesman can traverse the cities exactly one time. Initially we select parent1 & parent2 by Roulette wheel selection philosophy. We have applied one point crossover operator on the parent and produce the new child. Again we apply the mutation operator on offspring and created new child. But the no. of bits (cities) in a chromosome will be inverted by the mutation operator, that is depended on mutation probability (p m ). So, one generation contain 6 individual (chromosome). We have to count fitness (minimum cost) of the individuals in each generation. We have to select two individuals with best (min fitness) fitness for the next generation.