A discrete chicken swarm optimization for traveling salesman problem

Discrete Cat Swarm Optimization to Resolve the Traveling Salesman Problem

This research paper presents an adaptation of the cat swarm optimization (CSO) to solve the traveling salesman problem (TSP). This evolutionary algorithm appeared in 2007 by Chu and Tsai for optimization problems in the continuous case. To solve TSP, which is a discrete problem, we will describe the various operators and operations performed in two different modes of this algorithm, which is th...

Particle Swarm Optimization Algorithm for the Traveling Salesman Problem

Particle swarm optimization, PSO, is an evolutionary computation technique inspired in the behavior of bird flocks. PSO algorithms were first introduced by Kennedy & Eberhart (1995) for optimizing continuous nonlinear functions. The fundamentals of this metaheuristic approach rely on researches where the movements of social creatures were simulated by computers (Reeves, 1983; Reynolds, 1987; He...

An Improved Particle Swarm Optimization for Traveling Salesman Problem

To compensate for the shortcomings of existing methods used in TSP (Traveling Salesman Problem), such as the accuracy of solutions and the scale of problems, this paper proposed an improved particle swarm optimization by using a self-organizing construction mechanism and dynamic programming algorithm. Particles are connected in way of scale-free fully informed network topology map. Then dynamic...

A Modified Discrete Particle Swarm Optimization Algorithm for the Generalized Traveling Salesman Problem

Solve Traveling Salesman Problem Using Particle Swarm Optimization Algorithm

The traveling salesman problem (TSP) is one of the most widely studied NP-hard combinatorial optimization problems and traditional genetic algorithm trapped into the local minimum easily for solving this problem. Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. Compared with the genetic algorithm...