A two-phase tabu-evolutionary algorithm for the 0–1 multidimensional knapsack problem

Lai, Xiangjing; Hao, Jin‐Kao; Glover, Fred; Lu, Zhengding

doi:10.1016/j.ins.2018.01.026

Cited by 41 publications

(24 citation statements)

References 40 publications

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“…However, devising heuristic solvers still remains to be a challenge. Among numerous metaheuristic proposals for the MDKP problem, the currently best performing ones are the DQPSO algorithm from [44] and the TPTEA algorithm from [45].…”

Section: Multi Dimensional Knapsack Problemmentioning

confidence: 99%

“…Concerning computation time, in [37] it is stated that CC 2 FS requires on average 0.21 s, FastMWDS requires 0.83 s, and FastDS requires 22.19 s to obtain the best solutions of each run. ACO-CPL + neg is somewhat slower by requiring on average 36.14 s. In the context of the MDKP, we compare ACO-CPL + neg to the current state-of-the-art algorithms: a sophisticated particle swarm optimization algorithm (DQPSO) from [44], published in 2020, and a powerful evolutionary algorithm (TPTEA) from [45], published in 2018. As these two algorithms-in their original papers-were applied to the 90 benchmark problems used in this work, it was not required to conduct additional experiments with ACO-CPL + neg .…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Nurcahyadi

Blum

2021

Mathematics

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Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

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Section: Multi Dimensional Knapsack Problemmentioning

confidence: 99%

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Nurcahyadi

Blum

2021

Mathematics

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“…This multiverse algorithm uses a transfer function mechanism to perform binarization. Finally, a two-phase tabu-evolutionary algorithm was developed by Lai et al [37] to address large instances of the MKP.…”

Section: Multidimensional Knapsack Problemmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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“…First, stochastic local search has been quite successful in solving numerous combinatorial problems [15]. Second, for many knapsack problems, the best performing algorithms are based on local optimization approaches; e.g., multidimensional knapsack problem [11,18,25], multidemand multidimensional knapsack problem [5,19], multiple-choice multidimensional knapsack problem [6,16], quadratic knapsack problem [8,27], quadratic multiple knapsack problem [7,23] and generalized quadratic knapsack problem [2]. In this work, we show that the discrete optimization approach based on stochastic local search is also quite valuable and effective for solving the SUKP.…”

Section: (Sukp )mentioning

confidence: 99%

Iterated two-phase local search for the Set-Union Knapsack Problem

Wei

Hao

2019

Future Generation Computer Systems

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The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1 knapsack problem. Given a set of weighted elements and a set of items with profits where each item is composed of a subset of elements, the SUKP involves packing a subset of items in a capacity-constrained knapsack such that the total profit of the selected items is maximized while their weights do not exceed the knapsack capacity. In this work, we present an effective iterated two-phase local search algorithm for this NP-hard combinatorial optimization problem. The proposed algorithm iterates through two search phases: a local optima exploration phase that alternates between a variable neighborhood descent search and a tabu search to explore local optimal solutions, and a local optima escaping phase to drive the search to unexplored regions. We show the competitiveness of the algorithm compared to the state-of-the-art methods in the literature. Specifically, the algorithm discovers 18 improved best results (new lower bounds) for the 30 benchmark instances and matches the best-known results for the 12 remaining instances. We also report the first computational results with the general CPLEX solver, including 6 proven optimal solutions. Finally, we investigate the effectiveness of the key ingredients of the algorithm on its performance.

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A two-phase tabu-evolutionary algorithm for the 0–1 multidimensional knapsack problem

Cited by 41 publications

References 40 publications

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

Iterated two-phase local search for the Set-Union Knapsack Problem

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