Genetic Algorithm for TSP Problem

Abstract

Abstract. The TSP is a typical NP problem. Therefore it is very important to the optimization for solving TSP problem. The genetic algorithm is one of ideal methods in solving it. However, the traditional genetic algorithm has the problems of slow convergence speed and local optimum. Aiming at the existing problems, the paper improved various operations and operators of traditional genetic algorithm, so that a better solution was put forward to solve the problem of local optimum prematurely. After making the selection operation, the elitist strategy was introduced to ensure the quality of the population gene; using adaptive mutation operator in mutation operation to improve the mutation quality and search result of algorithm. Genetic algorithm has characteristics of accurate result and fast convergence in solving TSP problem.

Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem

“…Genetic Algorithm GA (Sun, 2015) is a popular optimization technique that mimics the process of natural selection. It has been used to solve the TSP, and several variants of GA have been proposed.…”

“…In recent years, significant progress has been in developing powerful and general approximate methods known as metaheuristics. These methods, which include neighborhood approaches such as simulated annealing and tabu search (Prajapati et al, 2020) and evolutionary algorithms such as genetic algorithms (Sun, 2015) and evolutionary strategies (Slowik & Kwasnicka, 2020), have enabled the development of approximate solutions for large-scale classical optimization problems and previously unmanageable applications (Ezugwu et al, 2021). Metaheuristics have gained increasing attention in operations research and artificial intelligence in recent years.…”

“…Genetic Algorithm (GA) (Sun, 2015) Genetic Programming (GP) (Koza & Poli, 2005) Evolutionary programming (EP) (Opara & Arabas, 2019) Biogeography Based Optimizer (BBO) (Simon, 2008) Tree-Seed Algorithm (TS) (Cinar et al, 2020) Human algorithms Harmony Search (HS) (Lee & Geem, 2004) Imperialist Competitive Algorithm (ICA) (Atashpaz-Gargari & Lucas, 2007) Tabu Search (TS) (Barbarosoglu & Ozgur, 1999) Heat Exchange Optimization (TEO) (Kaveh & Dadras, 2017) The study of optimization and NP-hard problem-solving, including metaheuristics, has been influenced by the behavior of animals in nature (Tanaev et al, 1994). One well-known and extensively studied problem in this field is the traveling salesman problem (TSP) (Mzili et al, 2020), which belongs to the class of NP-hard optimization problems.…”