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Networks and Spatial Economics

, Volume 12, Issue 3, pp 421–439 | Cite as

An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem

  • Abdolsalam Ghaderi
  • Mohammad Saeed Jabalameli
  • Farnaz Barzinpour
  • Ragheb Rahmaniani
Article

Abstract

Location-allocation problems are a class of complicated optimization problems that determine the location of facilities and the allocation of customers to the facilities. In this paper, the uncapacitated continuous location-allocation problem is considered, and a particle swarm optimization approach, which has not previously been applied to this problem, is presented. Two algorithms including classical and hybrid particle swarm optimization algorithms are developed. Local optima of the problem are obtained by two local search heuristics that exist in the literature. These algorithms are combined with particle swarm optimization to construct an efficient hybrid approach. Many large-scale problems are used to measure the effectiveness and efficiency of the proposed algorithms. Our results are compared with the best algorithms in the literature. The experimental results show that the hybrid PSO produces good solutions, is more efficient than the classical PSO, and is competitive with the best results from the literature. Additionally, the proposed hybrid PSO found better solutions for some instances than did the best known solutions in the literature.

Keywords

Location Allocation Networks Local search Particle swarm optimization Hybrid algorithm 

Abbreviations

PSO

Particle swarm optimization

HPSO

Hybrid PSO

LA

Location-allocation

UCLAP

Uncapacitated continuous location-allocation problem

NP

Non-polynomial

SA

Simulated annealing

TS

Tabu search

GA

Genetic algorithm

VNS

Variable neighborhood search

NN

Neural network

LS

Local search

ALA

Alternate location-allocation

CH

Interchange heuristic

MALT

Multi-start alternate algorithm

VND

Variable neighborhood descent

VNDS

Variable neighborhood decomposition search

MA

Memetic algorithm

Notes

Acknowledgement

The authors wish to thank Prof. Michael Kuby for his helpful suggestions and anonymous referees for their valuable comments.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Abdolsalam Ghaderi
    • 1
  • Mohammad Saeed Jabalameli
    • 1
  • Farnaz Barzinpour
    • 1
  • Ragheb Rahmaniani
    • 1
  1. 1.Department of Industrial EngineeringIran University of Science and TechnologyTehranIran

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