Most recent issue published online in the International Journal of Metaheuristics.
International Journal of Metaheuristics
http://www.inderscience.com/browse/index.php?journalID=271&year=2022&vol=8&issue=1
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International Journal of Metaheuristics
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International Journal of Metaheuristics
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http://www.inderscience.com/browse/index.php?journalID=271&year=2022&vol=8&issue=1
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Variable neighbourhood search for binary integer programming problems
http://www.inderscience.com/link.php?id=127813
General solvers exist for several types of optimisation problems, with the commercially available solvers for mixed integer programming (MIP) being a prime example. Although binary integer programming (BIP) can be used to model a wide variety of important combinatorial optimisation problems, relatively few contributions have been made to develop heuristic algorithms for BIP. This paper examines whether variable neighbourhood search can be successfully used to tackle BIP instances, when avoiding very large neighbourhoods explored by the means of external MIP solvers. The results indicate that methods based on variable neighbourhood search are more successful than exact and heuristic commercial solvers on certain types of instances, while the opposite holds true on others. A general variable neighbourhood search proves very effective on instances with up to 200 variables, in particular some instances that are tightly constrained.
Variable neighbourhood search for binary integer programming problems
HÃ¥kon Bentsen; Lars Magnus Hvattum
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 1 - 26
General solvers exist for several types of optimisation problems, with the commercially available solvers for mixed integer programming (MIP) being a prime example. Although binary integer programming (BIP) can be used to model a wide variety of important combinatorial optimisation problems, relatively few contributions have been made to develop heuristic algorithms for BIP. This paper examines whether variable neighbourhood search can be successfully used to tackle BIP instances, when avoiding very large neighbourhoods explored by the means of external MIP solvers. The results indicate that methods based on variable neighbourhood search are more successful than exact and heuristic commercial solvers on certain types of instances, while the opposite holds true on others. A general variable neighbourhood search proves very effective on instances with up to 200 variables, in particular some instances that are tightly constrained.]]>
10.1504/IJMHEUR.2022.127813
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 1 - 26
HÃ¥kon Bentsen
Lars Magnus Hvattum
Faculty of Logistics, Molde University College, Norway ' Faculty of Logistics, Molde University College, Norway
black-box solver
0-1 integer programming
variable neighbourhood descent
VND
mathematical programming
2022-12-19T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
8
1
1
26
2022-12-19T23:20:50-05:00
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Hybrid metaheuristics for the periodic open arc routing problem
http://www.inderscience.com/link.php?id=127800
This work considers the periodic open arc routing problem (POCARP) that models the meter reader application. This application is very interesting when the routes are planned on horizon of several periods. We develop two approaches to solve the problem under study: the first one is based on hybrid genetic algorithm with a specific crossover and the second one on hybrid ant colony method combined with an insertion heuristic. The two proposed algorithms are hybridised with a local search procedure that exploits several moves (relocate, swap, 2-opt and change combination). The objective of the problem is to find a combination of service periods for each task as well as the feasible routes of each period by using a predefined number of available vehicles that minimise the total travelling distance over the multi-period horizon. We extended the optimal splitting procedure to generate and evaluate solutions. We compared our approaches with one of the most important insertion heuristics adapted to this problem. Computational experiments are conducted on a set of generated benchmark instances and indicate that the proposed metaheuristics dominate the good insertion heuristic.
Hybrid metaheuristics for the periodic open arc routing problem
Ali Kansou; Bilal Kanso
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 27 - 50
This work considers the periodic open arc routing problem (POCARP) that models the meter reader application. This application is very interesting when the routes are planned on horizon of several periods. We develop two approaches to solve the problem under study: the first one is based on hybrid genetic algorithm with a specific crossover and the second one on hybrid ant colony method combined with an insertion heuristic. The two proposed algorithms are hybridised with a local search procedure that exploits several moves (relocate, swap, 2-opt and change combination). The objective of the problem is to find a combination of service periods for each task as well as the feasible routes of each period by using a predefined number of available vehicles that minimise the total travelling distance over the multi-period horizon. We extended the optimal splitting procedure to generate and evaluate solutions. We compared our approaches with one of the most important insertion heuristics adapted to this problem. Computational experiments are conducted on a set of generated benchmark instances and indicate that the proposed metaheuristics dominate the good insertion heuristic.]]>
10.1504/IJMHEUR.2022.127800
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 27 - 50
Ali Kansou
Bilal Kanso
Department of Computer Science, Lebanese University, Beirut, Lebanon ' Department of Computer Science, Lebanese University, Beirut, Lebanon
metaheuristics
open arc routing problem
multi period
genetic algorithm
ant colony algorithm
2022-12-19T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
8
1
27
50
2022-12-19T23:20:50-05:00
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Solving the capacitated vehicle routing problem with two-dimensional loading constraints using a parallel VNS approach
http://www.inderscience.com/link.php?id=127802
A combination of the two most important problems in logistics called the capacitated vehicle routing problem with two-dimensional loading constraint is studied in this article. It aims in designing the most appropriate pathways, starting and terminating at a central depot, minimising the total transportation cost with a homogenous fleet of vehicles. Items loaded in each vehicle's trip must satisfy the two-dimensional of orthogonal packing constraint. Since the problem is an NP-hard optimisation problem, a large number of approaches have been proposed. Whereas, finding the exact solution via optimisation is still a challenging problem due to their complexity. In order to increase the exploration in the solution space, we propose the parallel variable neighbourhood search algorithm. It is tested with 180 benchmark instances and compared with state-of-the-art approaches. The results showed that our approach is competitive in terms of the quality of solutions found.
Solving the capacitated vehicle routing problem with two-dimensional loading constraints using a parallel VNS approach
Ines Sbai; Saoussen Krichen; Olfa Limam
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 51 - 78
A combination of the two most important problems in logistics called the capacitated vehicle routing problem with two-dimensional loading constraint is studied in this article. It aims in designing the most appropriate pathways, starting and terminating at a central depot, minimising the total transportation cost with a homogenous fleet of vehicles. Items loaded in each vehicle's trip must satisfy the two-dimensional of orthogonal packing constraint. Since the problem is an NP-hard optimisation problem, a large number of approaches have been proposed. Whereas, finding the exact solution via optimisation is still a challenging problem due to their complexity. In order to increase the exploration in the solution space, we propose the parallel variable neighbourhood search algorithm. It is tested with 180 benchmark instances and compared with state-of-the-art approaches. The results showed that our approach is competitive in terms of the quality of solutions found.]]>
10.1504/IJMHEUR.2022.127802
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 51 - 78
Ines Sbai
Saoussen Krichen
Olfa Limam
LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunisia ' LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunisia ' LARODEC Laboratory, Institut Supérieur d'Informatique de Tunis, Université de Tunis El Manar, Tunisia
routing
packing
parallel variable neighbourhood search
2L-CVRP
vehicle routing problem
2022-12-19T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
8
1
51
78
2022-12-19T23:20:50-05:00
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A multi-stage genetic algorithm for instance selection dedicated to k nearest neighbours classification: application to robot wall following problem
http://www.inderscience.com/link.php?id=127832
K nearest neighbours algorithm is a classic, well studied yet a promising classification technique with high accuracy and best learning time compared to other classification algorithms. The goal is to overcome its slow classification time using instance selection by eliminating redundant and erroneous data. The instance selection problem has been classified as a non-deterministic polynomial time hard problem. In this article, the aim is to keep up with real time applications such as robotics with limited memory, while maintaining the fast-learning speed and high classification accuracy. We introduce a new multistage genetic algorithm for instance selection consisting of successive genetic instance selection stages with iterative search space reduction. The results witnessed an extreme reduction in classification time, reaching 99% without any significant penalty in the accuracy. It has been tested successfully using real traces of robot wall following navigation datasets and favourably compared to other approaches using various datasets.
A multi-stage genetic algorithm for instance selection dedicated to k nearest neighbours classification: application to robot wall following problem
Sarah Madi; Ahmed Riadh Baba-Ali
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 79 - 96
K nearest neighbours algorithm is a classic, well studied yet a promising classification technique with high accuracy and best learning time compared to other classification algorithms. The goal is to overcome its slow classification time using instance selection by eliminating redundant and erroneous data. The instance selection problem has been classified as a non-deterministic polynomial time hard problem. In this article, the aim is to keep up with real time applications such as robotics with limited memory, while maintaining the fast-learning speed and high classification accuracy. We introduce a new multistage genetic algorithm for instance selection consisting of successive genetic instance selection stages with iterative search space reduction. The results witnessed an extreme reduction in classification time, reaching 99% without any significant penalty in the accuracy. It has been tested successfully using real traces of robot wall following navigation datasets and favourably compared to other approaches using various datasets.]]>
10.1504/IJMHEUR.2022.127832
International Journal of Metaheuristics, Vol. 8, No. 1 (2022) pp. 79 - 96
Sarah Madi
Ahmed Riadh Baba-Ali
Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumedienne, BP 32 El Alia, Bab Ezzouar Algiers, 16111, Algeria ' Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumedienne, BP 32 El Alia, Bab Ezzouar Algiers, 16111, Algeria
machine learning
instance selection
K nearest neighbours
KNN classification
genetic algorithm
wall following
2022-12-19T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
8
1
79
96
2022-12-19T23:20:50-05:00