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Article
Publication date: 3 November 2014

John H Drake, Matthew Hyde, Khaled Ibrahim and Ender Ozcan

Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose…

Abstract

Purpose

Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem

Design/methodology/approach

Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances.

Findings

The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.

Originality/value

In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

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Article
Publication date: 19 July 2019

Soukaina Laabadi, Mohamed Naimi, Hassan El Amri and Boujemâa Achchab

The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover…

Abstract

Purpose

The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space.

Design/methodology/approach

The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time.

Findings

The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP.

Practical implications

Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems.

Originality/value

This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.

Details

Engineering Computations, vol. 36 no. 7
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 1 December 2020

Yupeng Zhou, Mengyu Zhao, Mingjie Fan, Yiyuan Wang and Jianan Wang

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial…

Abstract

Purpose

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial optimization, which has rich application scenarios. Although some researchers performed effective algorithms on normal-sized instances, the authors found these methods deteriorated rapidly as the scale became larger. Therefore, the authors design an efficient yet effective algorithm to solve this large-scale optimization problem, making it applicable to real-world cases under the era of big data.

Design/methodology/approach

The authors develop three targeted strategies and adjust them into the adaptive tabu search framework. Specifically, the dynamic item scoring tries to select proper items into the knapsack dynamically to enhance the intensification, while the age-guided perturbation places more emphasis on the diversification of the algorithm. The lightweight neighborhood updating simplifies the neighborhood operators to reduce the algorithm complexity distinctly as well as maintains potential solutions. The authors conduct comparative experiments against currently best solvers to show the performance of the proposed algorithm.

Findings

Statistical experiments show that the proposed algorithm can find 18 out of 24 better solutions than other algorithms. For the remaining six instances on which the competitor also achieves the same solutions, ours performs more stably due to its narrow gap between best and mean value. Besides, the convergence time is also verified efficiency against other algorithms.

Originality/value

The authors present the first implementation of heuristic algorithm for solving large-scale set-union knapsack problem and achieve the best results. Also, the authors provide the benchmarks on the website for the first time.

Details

Data Technologies and Applications, vol. 55 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

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Article
Publication date: 29 July 2014

José Alexandre Matelli, Jonny C. Silva and Edson Bazzo

The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem

Abstract

Purpose

The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.

Design/methodology/approach

The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.

Findings

The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.

Originality/value

The expert system approach is not a “blind” method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

Details

Engineering Computations, vol. 31 no. 6
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 6 June 2008

Hamed Shah‐Hosseini

The purpose of this paper is to test the capability of a new population‐based optimization algorithm for solving an NP‐hard problem, called “Multiple Knapsack Problem”, or MKP.

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Abstract

Purpose

The purpose of this paper is to test the capability of a new population‐based optimization algorithm for solving an NP‐hard problem, called “Multiple Knapsack Problem”, or MKP.

Design/methodology/approach

Here, the intelligent water drops (IWD) algorithm, which is a population‐based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the proposed algorithm is used to solve the MKP.

Findings

The proposed IWD algorithm for the MKP is tested by standard problems and the results demonstrate that the proposed IWD‐MKP algorithm is trustable and promising in finding the optimal or near‐optimal solutions. It is proved that the IWD algorithm has the property of the convergence in value.

Originality/value

This paper introduces the new optimization algorithm, IWD, to be used for the first time for the MKP and shows that the IWD is applicable for this NP‐hard problem. This research paves the way to modify the IWD for other optimization problems. Moreover, it opens the way to get possibly better results by modifying the proposed IWD‐MKP algorithm.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 1 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 31 December 2006

Maria Chantzara and Miltiades Anagnostou

The successful provision of context‐awareness in pervasive environments requires the support of autonomic management facilities that provide ways to efficiently acquire…

Abstract

The successful provision of context‐awareness in pervasive environments requires the support of autonomic management facilities that provide ways to efficiently acquire and use contextual information. This paper claims that in order to offer viable context‐aware services, the issue of context imperfection and aging as well as the alignment of the context information that is used by a service with the customized service objectives should be taken into account. It presents an approach for managing the selection of context sources considering the freshness and actuality of the available information, and dynamically adapting to any source change and failure. Accordingly, there is no need to know beforehand the context sources to obtain the required information, but a quality‐aware discovery of the sources is envisioned. Finally, the proposed approach allows services to be ported easily to an environment with a different set of context sources.

Details

International Journal of Pervasive Computing and Communications, vol. 2 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 7 August 2009

Dariusz Gąsior

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the…

Abstract

Purpose

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the most common probabilistic description of the uncertainty cannot be used and another approach should be applied.

Design/methodology/approach

The uncertain versions of admission control problem with quality of service requirements are considered. The uncertain variables are used to describe possible values of the unknown parameters in computer networks.

Findings

Given are formulations for the admission control problem in computer networks with unknown values of the capacities based on the network utility maximization concept. Solution algorithms for all these problems are proposed.

Research limitations/implications

It is assumed that an expert can describe possible values of uncertain network parameters in the form of a certainty distribution. Then the formalism of uncertain variables is applied and the knowledge of an expert is modelled with the use of certainty distributions. Decisions strongly depends on the quality of an expert's knowledge.

Practical implications

Obtained admission control algorithms can be useful for planning and designing of computer networks.

Originality/value

A new approach to the admission control problem in computer networks in the presence of uncertainty, in the case when the uncertain variable can be applied, is proposed and discussed.

Details

Kybernetes, vol. 38 no. 7/8
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 29 June 2012

Yang Liu and Yasser Mohamed

Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is…

Abstract

Purpose

Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is usually a function required in any scheduling process. Performing such allocation under a dynamic and diverse set of constraints adds more complexity to the problem. This study seeks a structured approach for representing resources and their allocation to different activities through the use of an agent‐oriented modelling framework.

Design/methodology/approach

A model is developed for a real case of assembly operations of industrial construction modules. The model follows a multi‐agent resource allocation structure and is implemented within an agent‐based simulation environment. The model is used to evaluate the effects of different optimization algorithms and modelling parameters on the generation of a construction schedule. Different experiments run through the model and their results are analyzed and discussed.

Findings

The model showed sensitivity only under large and continuous workloads. Overall the structured approach followed in developing the model provided a flexible medium for experimenting with different elements of the resource allocation problem.

Research limitations/implications

The work is limited to the studied case and the results cannot be generalized beyond similar cases. The modelling approach used in the study provides a platform that can facilitate future research in construction resource allocation strategies.

Originality/value

The presented work demonstrates a new approach for modelling construction resource allocation problems that enables structured experimentation with alternative allocation algorithms. It also presents a novel way for modelling modular industrial construction operations.

Details

Engineering, Construction and Architectural Management, vol. 19 no. 4
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 3 November 2014

Christopher Garcia

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects…

Abstract

Purpose

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem.

Design/methodology/approach

The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required.

Findings

The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions.

Practical implications

The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement.

Originality/value

This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

Details

Kybernetes, vol. 43 no. 9/10
Type: Research Article
ISSN: 0368-492X

Keywords

Content available
Article
Publication date: 3 November 2014

Magnus Ramage, David Chapman and Chris Bissell

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146

Abstract

Details

Kybernetes, vol. 43 no. 9/10
Type: Research Article
ISSN: 0368-492X

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