International Journal of Metaheuristics http://www.inderscience.com/browse/index.php?journalID=271&year=2011&vol=1&issue=4 Inderscience Publishers Ltd en-uk International Journal of Metaheuristics 1755-2176 1755-2184 © 2011 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijmheur_scoverijmheur.jpg http://www.inderscience.com/browse/index.php?journalID=271&year=2011&vol=1&issue=4 http://www.inderscience.com/link.php?id=44302 Differential evolution (DE) has been extensively applied to continuous problems, its mechanics naturally lending themselves to such. While some efforts have been made to adapt it to combinatorial problems, these have largely been problem specific and have not dealt extensively with constraint handling beyond penalty approaches. In this paper, a simple and generic strategy, relying on pre-developed heuristic units, is applied to DE and the generalised assignment problem. In addition, a simple, parameter-free approach to adapting control parameters is used. The results are competitive with other well established meta-heuristics. However, there is still scope for further improvement in the way that DE may be applied to constrained combinatorial optimisation. Differential evolution for a constrained combinatorial optimisation problem
Marcus Randall
International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 279 - 297
Differential evolution (DE) has been extensively applied to continuous problems, its mechanics naturally lending themselves to such. While some efforts have been made to adapt it to combinatorial problems, these have largely been problem specific and have not dealt extensively with constraint handling beyond penalty approaches. In this paper, a simple and generic strategy, relying on pre-developed heuristic units, is applied to DE and the generalised assignment problem. In addition, a simple, parameter-free approach to adapting control parameters is used. The results are competitive with other well established meta-heuristics. However, there is still scope for further improvement in the way that DE may be applied to constrained combinatorial optimisation.

]]> 10.1504/IJMHEUR.2011.044302 International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 279 - 297 Marcus Randall School of Information Technology, Bond University, University Drive, Queensland 4229, Australia differential evolution constrained combinatorial optimisation extremal optimisation generalised assignment problem GAP constraint handling. 2011-12-19T23:20:50-05:00 1 4 279 297 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44314 A priority-considering heuristic approach is proposed to solve the multiple container loading problem, i.e., the problem of packing a given set of three-dimensional rectangular items into multiple containers to make the maximum use of the container space. The items with large volume are usually difficult to make efficient use of the container space. These items are set higher priority over other items, and preferentially assigned into the containers. Within the proposed approach an algorithm is used for solving the single container loading problem under the constraint of priority. The proposed approach achieves excellent results for the test cases suggested by Ivancic et al. and Mohanty et al. with reasonable computing time. A priority-considering approach for the multiple container loading problem
Jidong Ren; Yajie Tian; Tetsuo Sawaragi
International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 298 - 316
A priority-considering heuristic approach is proposed to solve the multiple container loading problem, i.e., the problem of packing a given set of three-dimensional rectangular items into multiple containers to make the maximum use of the container space. The items with large volume are usually difficult to make efficient use of the container space. These items are set higher priority over other items, and preferentially assigned into the containers. Within the proposed approach an algorithm is used for solving the single container loading problem under the constraint of priority. The proposed approach achieves excellent results for the test cases suggested by Ivancic et al. and Mohanty et al. with reasonable computing time.

]]> 10.1504/IJMHEUR.2011.044314 International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 298 - 316 Jidong Ren; Yajie Tian; Tetsuo Sawaragi Graduate School of Engineering, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan. ' Graduate School of Engineering, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan. ' Graduate School of Engineering, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan container loading problem CLP bin packing problem knapsack problem heuristics priority multiple containers. 2011-12-19T23:20:50-05:00 1 4 298 316 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44356 The class of problems known as quadratic zero-one (binary) unconstrained optimisation has provided access to a vast array of combinatorial optimisation problems, allowing them to be expressed within the setting of a single unifying model. A gap exists, however, in addressing polynomial problems of degree greater than 2. To bridge this gap, we provide methods for efficiently executing core search processes for the general polynomial unconstrained binary (PUB) optimisation problem. A variety of search algorithms for quadratic optimisation can take advantage of our methods to be transformed directly into algorithms for problems where the objective functions involve arbitrary polynomials. Part 1 of this paper (Glover et al., 2011) provided fundamental results for carrying out the transformations and described coding and decoding procedures relevant for efficiently handling sparse problems, where many coefficients are 0, as typically arise in practical applications. In the present part 2 paper, we provide special algorithms and data structures for taking advantage of the basic results of part 1. We also disclose how our designs can be used to enhance existing quadratic optimisation algorithms. Polynomial unconstrained binary optimisation part 2
Fred Glover; Jin-Kao Hao; Gary Kochenberger
International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 317 - 354
The class of problems known as quadratic zero-one (binary) unconstrained optimisation has provided access to a vast array of combinatorial optimisation problems, allowing them to be expressed within the setting of a single unifying model. A gap exists, however, in addressing polynomial problems of degree greater than 2. To bridge this gap, we provide methods for efficiently executing core search processes for the general polynomial unconstrained binary (PUB) optimisation problem. A variety of search algorithms for quadratic optimisation can take advantage of our methods to be transformed directly into algorithms for problems where the objective functions involve arbitrary polynomials. Part 1 of this paper (Glover et al., 2011) provided fundamental results for carrying out the transformations and described coding and decoding procedures relevant for efficiently handling sparse problems, where many coefficients are 0, as typically arise in practical applications. In the present part 2 paper, we provide special algorithms and data structures for taking advantage of the basic results of part 1. We also disclose how our designs can be used to enhance existing quadratic optimisation algorithms.

]]> 10.1504/IJMHEUR.2011.044356 International Journal of Metaheuristics, Vol. 1, No. 4 (2011) pp. 317 - 354 Fred Glover; Jin-Kao Hao; Gary Kochenberger 1OptTek Systems, Inc., 2241 17th Street, Boulder, CO 80302, USA. ' Laboratoire d'Etude et de Recherche en Informatique (LERIA), Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers Cedex 01, France. ' School of Business Administration, University of Colorado at Denver, Denver, CO 80217, USA zero-one optimisation unconstrained polynomial optimisation metaheuristics computational efficiency polynomial unconstrained binary optimisation quadratic optimisation. 2011-12-19T23:20:50-05:00 1 4 317 354 2011-12-19T23:20:50-05:00