Design and Implementation of a Combinatorial Optimization Multi-population Meta-heuristic for Solving Vehicle Routing Problems

Osaba, Eneko; Díaz, Fernando

doi:10.9781/ijimai.2016.4213

Cited by 3 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the hybrid ant colony optimization algorithm is an effective method for solving the open vehicle routing problem (Marinakis and Marinaki, 2014) [15]. It is due to the current environmental requirements with multipopulation metaheuristics for solving vehicle routing problems (Osaba and Díaz, 2016) [16]. It defines a decision model using the proposed methodological Jenks optimization approach for possible optimal locations of various facilities which can be used by the transport infrastructure (Fraile et al, 2016) [17].…”

Section: Vehicle Routing Optimizationmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Liu

Shan

Zhang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

show abstract

Section: Vehicle Routing Optimizationmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Liu

Shan

Zhang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The paper includes a review on recent works in these areas. Osaba and Díaz [13] present the work developed during PhD thesis of first author, related to multi-population meta-heuristics for solving vehicle routing problems. The main contribution of this thesis is a metaheuristic called Golden-Ball, which is conceptually original, based on soccer concepts.…”

Section: T He International Journal Of Interactive Multimedia and Artmentioning

confidence: 99%

Editor's Note

Verdú¹

2016

IJIMAI

View full text Add to dashboard Cite

“…It uses different principles of soccer to solve combinatorial optimization problems. The quality of this technique is demonstrated applying it to four combinatorial problems [23]: Asymmetric traveling salesman problem (ATSP) [24], Vehicle Routing Problem with Backhauls (VRPB) [25], [26], n-Queen Problem (NQP) [27], One-Dimensional Bin Packing Problem (BPP) [28].This algorithm is a promising metaheuristic to solve combinatorial optimization problems [23].…”

Section: A Golden Ball Metaheuristicmentioning

confidence: 99%

Hybrid Algorithm for Solving the Quadratic Assignment Problem

Riffi¹,

Sayoti²

2019

IJIMAI

View full text Add to dashboard Cite

T HE quadratic assignment problem (QAP) is one of the known classical combinatorial optimization problems, in 1976 Sahni and Gonzalez [1] proved that the QAP belongs to the class of NP-hard problems [1]. It was introduced for the first time by Koopmans and Beckmann in 1957 [2]; its purpose is to assign n facilities to n fixed locations with a given flow matrix of facilities and distance matrix of locations in order to minimize the total assignment cost. This problem is applied in various fields such as hospital layout [3], scheduling parallel production lines [4] and analyzing chemical reactions for organic compounds [5].Many recent hybrid approaches have improved performance in solving QAP such as genetic algorithm hybridized with tabu search method [6], ant colony optimization mixed with local search method [7] and ant colony optimization combined with genetic algorithm and local search method [8]. Recently the hybrid algorithms are much proposed and used by many researchers to find optimal or near optimal solutions for the QAP.In this paper we propose a new competitive approach when compared with other existing methods in the literature. The golden ball algorithm mixed with simulated annealing (GBSA) is considered here as a hybrid metaheuristic to apply in the quadratic assignment problem.This work presents an efficient adaptation of GBSA algorithm to the quadratic assignment problem (QAP). This algorithm is based on the concept of soccer; it guides the search by simulated annealing [9] to escape from the local optima. The suggested technique has never been proposed or tested with QAP. In this research we use some small, medium and large test problems for comparing our approach to other recent methods from literature. Our approach is able to explore effectively the search space; it reaches the known optimal solutions in less time.The rest of this paper is structured as follows: In section I, Introduction. In section II, Quadratic assignment problem formulation. In section III, Methods. In section IV, Results and discussion. In section V, Conclusion. KeywordsCombinatorial Optimization, Golden Ball Algorithm, Simulated Annealing, Quadratic Assignment Problem. AbstractThe Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literatu...

show abstract

Design and Implementation of a Combinatorial Optimization Multi-population Meta-heuristic for Solving Vehicle Routing Problems

Cited by 3 publications

References 3 publications

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Editor's Note

Hybrid Algorithm for Solving the Quadratic Assignment Problem

Contact Info

Product

Resources

About