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

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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.

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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

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Acknowledgement

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

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Authors and Affiliations

  1. Department of Industrial Engineering, Iran University of Science and Technology, P.C. 1684613114, Narmak, Tehran, Iran

    Abdolsalam Ghaderi, Mohammad Saeed Jabalameli, Farnaz Barzinpour & Ragheb Rahmaniani

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  1. Abdolsalam Ghaderi
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  2. Mohammad Saeed Jabalameli
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  3. Farnaz Barzinpour
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  4. Ragheb Rahmaniani
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Correspondence to Abdolsalam Ghaderi.

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Ghaderi, A., Jabalameli, M.S., Barzinpour, F. et al. An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem. Netw Spat Econ 12, 421–439 (2012). https://doi.org/10.1007/s11067-011-9162-y

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