Orthogonal Genetic Algorithm and its Application in Traveling Salesman Problem

Abstract

Abstract. The traveling salesman problem (TSP) is one of the most widely studied NP-hard combinatorial optimization problems. Its statement is deceptively simple, and yet it remains one of the most challenging problems and traditional genetic algorithm trapped into the local minimum easily for solving this problem. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive orthogonal local search to prevent local convergence to form a new orthogonal genetic algorithm. The new algorithm shows great efficiency in solving TSP with the problem scale under 300 under the experiment results analyze.

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

“…In constructing a powerful GA, edge swapping (ES) with a local search procedure is used to determine good combinations of building blocks of parent solutions for generating even better offspring solutions [22]. Another important contribution is the development of ES in generating even better offspring solutions from very high quality parent solutions at the final phase of the GA. An interesting feature is that a simple local search procedure was designed into ES to determine good combinations of the edges of parents.…”

“…This can be attributed to the computational cost accrued to population based search in GAs. However, the major reason arises from the fact that crossover operators require more computational cost to generate an offspring solution than do local search operators to evaluate a solution in the neighbourhood [22]. A local search algorithm"s ability to locate local optima with high accuracy complements the ability of genetic algorithms to capture a global view of the search space [23].…”