A novel quantum inspired cuckoo search for knapsack problems

Layeb, Abdesslem

doi:10.1504/ijbic.2011.042260

Cited by 162 publications

(56 citation statements)

References 22 publications

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“…The quantum differential evolution algorithm was applied to the knapsack problem in [53], combinatorial problems [54], and image threshold methods in [55]. Using the cuckoo search metaheuristic, a quantum algorithm was applied to the knapsack problem [56] and bin packing problem [57]. A 4 Complexity quantum ant colony optimization was applied to image threshold in [55].…”

Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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“…In order to evaluate the performance of the proposed TDDE algorithm for solving 0-1 knapsack problems, twenty 0-1 knapsack test instances are produced by using the method as recommended by [4], which is widely used in the field of 0-1 knapsack problem research [15,16,19]. To generate each 0-1 knapsack test instance, we randomly generate the weight W j and profit P j for item j, where j = 1, · · · , D; W j is a uniformly distributed random number in the range [5,20], P j is randomly generated from a uniform distribution in the range [50,100] and the maximum weight capacity of a knapsack C is set to:…”

Section: Methodsmentioning

confidence: 99%

“…Firstly, a temporary population P t+1 is created by combining the parent population P t with the offspring population O t at generation t. Then, the free energy of each individual in the temporary population P t+1 is calculated according to Equation (15). After that, the top M individuals with maximal free energy are chosen to delete from the temporary population P t+1 .…”

Section: Description Of Thermodynamical Selection Operator For Dementioning

confidence: 99%

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A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Guo

Yue

Zhang

et al. 2014

Entropy

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Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE), we present a novel discrete differential evolution (TDDE) for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances.

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“…QC brings new philosophy to optimization due to its underlying concepts. Recently, a growing theoretical and practical interest is devoted to researches on merging evolutionary computation and quantum computing [19][20][21][22]. The aim is to get benefit from quantum computing capabilities to enhance both efficiency and speed of classical evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Quantum Genetic Algorithm and Local Search based DPLL for Max 3-SAT Problems

Layeb¹,

Saïdouni²

2014

Appl. Math. Inf. Sci.

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Abstract:In this paper, we present a new framework for combining complete and incomplete methods in order to deal with the Max Sat problem. The objective is to find the best assignment for a set of Boolean variables, which gives the maximum of verified clauses in a Boolean formula. Unfortunately, this problem has been shown to be NP-hard (non-deterministic polynomial-time hard) if the number of variables per clause is greater than 3. The proposed approach is based on quantum inspired genetic principles and the well known exact algorithm DPLL. The underlying idea is to harness the optimization capabilities of quantum genetic algorithm to achieve good quality solutions for Max SAT problem. A new local search based on DPLL algorithm has been embodied in the quantum genetic algorithm leading to an efficient hybrid framework which achieves better balance between diversification and intensification search. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed hybrid approach.

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A novel quantum inspired cuckoo search for knapsack problems

Cited by 162 publications

References 22 publications

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

A Hybrid Quantum Genetic Algorithm and Local Search based DPLL for Max 3-SAT Problems

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