Tight oscillations tabu search for multidimensional knapsack problems with generalized upper bound constraints

Li, Vincent C.

doi:10.1016/j.cor.2004.04.020

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…Several of their results for these instances were considerably improved by Vasquez [35,36], through a tabu-search algorithm. After that, many studies proposed new tabu-search methods for the MKP [37][38][39]. In GA's field, CBGA has influenced a series of studies concerning new GA's operators and fitness-landscapes analysis [40][41][42][43][44], and still is used as reference for more recent works with univariate EDAs [45,46,17,18], particle swarm optimization [47,48], differential evolution [49].…”

Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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“…Critical solutions are important because they are near the feasibility boundary. To explore the neighborhood around the critical solutions, Li [24] proposed a tight oscillation scheme which considers the changes of both the objective function and feasibility in its choice rules. This oscillation swings back and forth around the feasibility boundary very closely till a duplicated solution has been found.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on the 10-minute allinstances average gap among these 12 cases, we select cases 1, 11, and 12 to represent our scatter search heuristic to compare with several methods in the literature including critical event tabu search (CETS) with local search [26], tight oscillation (TO) [24], and adaptive memory projection (AMP) [27]. The comparison in Table 5 shows the AMP approach has attained the best results overall.…”

Section: Computational Runsmentioning

confidence: 99%

A scatter search method for the multidimensional knapsack problem with generalized upper bound constraints

Liang

Sun

et al. 2012

Journal of the Chinese Institute of Industrial Engineers

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In this paper, a scatter search (SS) heuristic is proposed to solve the multidimensional knapsack problem with generalized upper bound constraints (GUBMKP). The method is organized according to the general structure of SS. We discuss the design and implementation for each of the components of SS. A greedy randomized adaptive search procedure is applied in order to diversify the initial solutions. In order to select diversified solutions to enter the reference set, we propose an algorithm based on the structure of generalized upper bound constraints. Several approaches of combining solutions are proposed to solve the problem. The computational results show the heuristic is competitive compared to the former leading method for the GUBMKP.

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“…Their several results were further improved by using tabu search algorithm in [18][19]. Then some other algorithms based on the new tabu search algorithm were proposed in [20][21][22]. Considering the new GA operators and fitness landscapes analysis, CBGA algorithms was used in series of studies in [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem

Taşgetiren

Pan

Kızılay

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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This paper presents a differential evolution algorithm with a variable neighborhood search to solve the multidimensional knapsack problem. Unlike the studies employing check and repair operators, we employ some sophisticated constraint handling methods to enrich the population diversity by taking advantages of infeasible solution within a predetermined threshold. We propose to a variable neighborhood search employing different mutation strategies to generate the trial population. The proposed algorithm in fact works on a continuous domain, but these real-values are converted to 0-1 binary values by using the sigmoid function. In order to enhance the solution quality, the differential evolution algorithm with a variable neighborhood search is combined with a binary swap local search algorithm. To the best of our knowledge, this is the first reported application of the differential evolution algorithm to solve the multidimensional knapsack problem in the literature. The proposed algorithm is tested on a benchmark instances from the OR-Library. Computational results show its efficiency in solving benchmark instances and its superiority to the best performing algorithms from the literature. Keywords-differential evolution, variable neighborhood search, binary local search, constraint handling, multidimensional knapsack problem.

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Tight oscillations tabu search for multidimensional knapsack problems with generalized upper bound constraints

Cited by 18 publications

References 9 publications

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

A scatter search method for the multidimensional knapsack problem with generalized upper bound constraints

A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem

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