Adjustability of a discrete particle swarm optimization for the dynamic TSP

Abstract

This paper presents a detailed study of the discrete particle swarm optimization algorithm (DPSO) applied to solve the dynamic traveling salesman problem which has many practical applications in planning, logistics and chip manufacturing. The dynamic version is especially important in practical applications in which new circumstances, e.g., a traffic jam or a machine failure, could force changes to the problem specification. The DPSO algorithm was enriched with a pheromone memory which is used to guide the search process similarly to the ant colony optimization algorithm. The paper extends our previous work on the DPSO algorithm in various ways. Firstly, the performance of the algorithm is thoroughly tested on a set of newly generated DTSP instances which differ in the number and the size of the changes. Secondly, the impact of the pheromone memory on the convergence of the DPSO is investigated and compared with the version without a pheromone memory. Moreover, the results are compared with two ant colony optimization algorithms, namely the MAX -MIN ant system (MMAS) and the population-based ant colony optimization (PACO). The results show that the DPSO is able to find high-quality solutions to the DTSP and its performance is competitive with the performance of the MMAS and the PACO algorithms. Moreover, the pheromone memory has a positive impact on Communicated by V. Loia.

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

“…The authors proposed their PSO algorithm with heterogeneous (non-uniform) parameter values; the parameters are set automatically for the critical PSO parameters based on discrete probability distributions. This approach proved to be more successful compared to the original PSO with homogeneous (uniform) parameter values [ 35 ]. Genetic algorithms are also used in several cases for the DTSP such as when [ 36 ] presenting an algorithm called the extended virtual loser genetic algorithm (eVLGA) or [ 37 ] presenting a genetic algorithm that feeds on Newton’s motion equation to show how route optimization can be improved when targets are constantly moving.…”

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

“…The combination of prioritized K-mean and genetic algorithm was used to optimize manufacturing related transportation processes [35]. Heuristic optimization methods are used in the case of NP-hard optimization problems: genetic algorithm was used to increase machine utilization, reduce throughput time and delivery delays [36,37], while discrete particle swarm optimization (PSO) was applied to solve the dynamic travelling salesman problem in chip manufacturing, where machine failure can force changes to the problem specification [38]. A typical application field of PSO is flow shop and job shop manufacturing [39] or the machine loading problem in flexible manufacturing systems, where the feeding process is generally robotized or automatized [40].…”

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

“…A (homogeneous) DPSO algorithm with pheromone was proposed in our previous work [ 8 ], and this section contains only a brief description. Adaptation to a discrete space forces some changes to the original PSO algorithm designed for solving continuous optimization problems.…”

“…Shi et al showed that the proposed algorithm is capable of solving the generalized TSP, in which the edge lengths do not satisfy the triangle inequality. The homogeneous and heterogeneous versions of the DPSO algorithm described in the present paper are based on work by Zhong et al [ 7 ] and on our previous DTSP variant [ 8 ]. In the implementation presented here, the particle position comprises a set of edges connecting TSP cities (nodes) and the corresponding probabilities of selecting the edges to the next solution (the next position of the particle).…”

“…Our previous work focused mainly on DPSO with homogeneous (uniform) parameter values [ 8 ]. In this paper, we extend our initial work on DPSO in which individual particles may have non-uniform (varying) values of the parameters [ 17 ].…”