A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem

Authors:
Yannis Marinakis;Magdalene Marinaki
Affiliations:
Technical University of Crete, Department of Production Engineering and Management, Decision Support Systems Laboratory, 73100 Chania, Greece;Technical University of Crete, Department of Production Engineering and Management, Industrial Systems Control Laboratory, 73100 Chania, Greece
Venue:
Computers and Operations Research
Year:
2010

Citing 9
Cited 12

A priori solution of a travelling salesman problem in which a random subset of the customers are visited

Operations Research
Swarm intelligence

Swarm intelligence
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Solving the Homogeneous Probabilistic Traveling Salesman Problem by the ACO Metaheuristic

ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Expanding Neighborhood GRASP for the Traveling Salesman Problem

Computational Optimization and Applications
Aggregation for the probabilistic traveling salesman problem

Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A hybrid scatter search for the probabilistic traveling salesman problem

Computers and Operations Research
A review of particle swarm optimization. Part I: background and development

Natural Computing: an international journal
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications

Natural Computing: an international journal

Estimation-based metaheuristics for the probabilistic traveling salesman problem

Computers and Operations Research
Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search

Applied Soft Computing
Particle swarm optimization for the economic and economic statistical designs of the control chart

Applied Soft Computing
Application of Particle Swarm Optimization to uniform and variable strength covering array construction

Applied Soft Computing
An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem

Applied Soft Computing
Meta-heuristic algorithms for solving a fuzzy single-period problem

Mathematical and Computer Modelling: An International Journal
DOE-based parameter tuning for local branching algorithm

International Journal of Metaheuristics
Location and allocation decisions for multi-echelon supply chain network - A multi-objective evolutionary approach

Expert Systems with Applications: An International Journal
A hybrid meta-heuristic algorithm for optimization of crew scheduling

Applied Soft Computing
DIMMA-Implemented Metaheuristics for Finding Shortest Hamiltonian Path Between Iranian Cities Using Sequential DOE Approach for Parameters Tuning

International Journal of Applied Metaheuristic Computing
Combinatorial neighborhood topology particle swarm optimization algorithm for the vehicle routing problem

EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Combinatorial expanding neighborhood topology particle swarm optimization for the vehicle routing problem with stochastic demands

Proceedings of the 15th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the classic PSO and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 13 out of 20 cases the proposed algorithm gives a new best solution.