A hybrid quantum particle swarm optimization for the Multidimensional Knapsack Problem

Haddar, Boukthir; Khemakhem, Mahdi; Hanafi, Saïd; Wilbaut, Christophe

doi:10.1016/j.engappai.2016.05.006

Cited by 80 publications

(32 citation statements)

References 55 publications

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“…e greatest advantage of the QPSO algorithm over the PSO algorithm is that the QPSO ameliorates the bug of PSO preferably and improves the search efficiency [42]. Now, it has been used to solve such nonlinear constrained optimization problems [42][43][44]. In the following, we present a brief introduction to the QPSO algorithm to solve the nonlinear constrained optimization problem (21).…”

Section: Parameters Estimation and Computationalmentioning

confidence: 99%

“…en, a new weighted fractional GM(1,1) (WFGM(1,1)) model, which involves the classical GM(1,1), the NIPGM(1,1), and the FGM(1,1) prediction models as special cases, is constructed. In addition, by defining a nonlinear constrained optimization problem for fitted values, the quantum particle swarm optimization (QPSO) method is adopted to find the best parameters [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Section: Parameters Estimation and Computationalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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“…The new PSO was tested on 135 benchmark problems from the OR-Library to prove its effectiveness. [113] applied a quantum binary approach that belongs to binarization techniques to PSO. The obtained algorithm is combined with local search method to solve 0/1 MKP, as well as a heuristic based on repair operator that uses problem-specific knowledge.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Laabadi¹,

Naimi²,

Amri³

et al. 2018

AJOR

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The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

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“…First, the two groups are required to have roughly the same average HSI level. The grouping method in this paper is as follows: Secondly, habitat , which is used for adding perturbation in operator (4), is selected from all habitats instead of only from the habitats in Group A or Group B. Thirdly, the two groups are mixed up at the end of each migration process and the population is regrouped at the beginning of each migration process. The second and third points show that although the habitats in the two groups are based on totally different topology to implement the migration process, the two groups can still adequately share their information and collaborate to move toward a more promising direction.…”

Section: Multitopology Migration Operatormentioning

confidence: 99%

“…EAs mimic various social behaviors existing in nature to solve the optimization problems. Some popular EAs include genetic algorithm (GA) [1,2], particle swarm optimization (PSO) [3,4], artificial immune system (AIS) [5], differentiable evolution (DE) [6,7], ant colony optimization (ACO) [8], artificial bee colony (ABC) [9,10], and simulated annealing algorithm (SA) [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

Wen

Chen

et al. 2017

Mathematical Problems in Engineering

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Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

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A hybrid quantum particle swarm optimization for the Multidimensional Knapsack Problem

Cited by 80 publications

References 55 publications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

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