Solving the multidimensional knapsack problems with generalized upper bound constraints by the adaptive memory projection method

Li, Vincent C.; Liang, Yun-Chia; Chang, Huan-Fu

doi:10.1016/j.cor.2011.10.016

Cited by 16 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

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

2014

View full text Add to dashboard Cite

“…By adopting the adaptive memory projection framework in Glover [13], Li et al [27] implemented a method that iteratively combines critical event tabu search with an MIP solver for the GUBMKP. We develop a scatter search heuristic for the GUBMKP in this study.…”

Section: Literature Reviewmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Plenty of methods were introduced to solve MKP in recent years including deterministic and approximate algorithms [9]. Some exact algorithms like dynamic programming [7,10], branch and bound algorithm [11] and hybrid algorithms [12,13] can solve small-scaled and medium-scaled problems within endurable time. As the number of items and constraints increase, the performance of exact algorithm declines rapidly and becomes intolerable.…”

Section: Introductionmentioning

confidence: 99%

Binary artificial algae algorithm for multidimensional knapsack problems

Zhang

et al. 2016

Applied Soft Computing

View full text Add to dashboard Cite

The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms.

show abstract

Solving the multidimensional knapsack problems with generalized upper bound constraints by the adaptive memory projection method

Cited by 16 publications

References 34 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

Binary artificial algae algorithm for multidimensional knapsack problems

Contact Info

Product

Resources

About