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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: 8 June 2015

Yu Lei, Maoguo Gong, Licheng Jiao and Yi Zuo

The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies…

Abstract

Purpose

The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies. Hyper-heuristic is a kind of representative methods. In hyper-heuristic, the high-level search is executed to construct heuristic lists by traditional methods (such as Tabu search, variable neighborhoods and so on). The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.

Design/methodology/approach

This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems. First, four graph coloring heuristics are employed to construct heuristic lists. Within the evolutionary algorithm framework, the iterative initialization is utilized to improve the number of feasible solutions in the population; meanwhile, the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space (high-level search). At last, two local search methods are combined to optimize the feasible solutions in the solution space (low-level search).

Findings

Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.

Originality/value

The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 30 September 2014

Jose M. Chaves-Gonzalez and Miguel A. Vega-Rodríguez

The purpose of this paper is to study the use of a heterogeneous and evolutionary team approach based on different sources of knowledge to address a real-world problem…

Abstract

Purpose

The purpose of this paper is to study the use of a heterogeneous and evolutionary team approach based on different sources of knowledge to address a real-world problem within the telecommunication domain: the frequency assignment problem (FAP). Evolutionary algorithms have been proved as very suitable strategies when they are used to solve NP-hard optimization problems. However, these algorithms can find difficulties when they fall into local minima and the generation of high-quality solutions when tacking real-world instances of the problem is computationally very expensive. In this scenario, the use of a heterogeneous parallel team represents a very interesting approach.

Design/methodology/approach

The results have been validated by using two real-world telecommunication instances which contain real information about two GSM networks. Contrary to most of related publications, this paper is focussed on aspects which are relevant for real communication networks. Moreover, due to the stochastic nature of metaheuristics, the results are validated through a formal statistical analysis. This analysis is divided in two stages: first, a complete statistical study, and after that, a full comparative study against results previously published.

Findings

Comparative study shows that a heterogeneous evolutionary proposal obtains better results than proposals which are based on a unique source of knowledge. In fact, final results provided in the work surpass the results of other relevant studies previously published in the literature.

Originality/value

The paper provides a complete study of the contribution provided by the different metaheuristics included in the team and the impact of using different sources of evolutionary knowledge when the system is applied to solve a real-world FAP problem. The conclusions obtained in this study represent an original contribution never reached before for FAP.

Details

Engineering Computations, vol. 31 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

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Book part
Publication date: 26 October 2017

Sudhanshu Joshi, Manu Sharma and Shalu Rathi

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary…

Abstract

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

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Article
Publication date: 16 April 2018

Marina Tsili, Eleftherios I. Amoiralis, Jean Vianei Leite, Sinvaldo R. Moreno and Leandro dos Santos Coelho

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and…

Abstract

Purpose

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting with each other. This paper aims to treat the transformer design optimization (TDO) as a multiobjective problem (MOP), to minimize the manufacturing cost and the total owing cost, taking into consideration design constraints.

Design/methodology/approach

To deal with this optimization problem, a new method is proposed that combines the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) with differential evolution, also applying lognormal distribution for tuning the scale factor and the beta distribution to adjust the crossover rate (UPS-DELFBC). The proposed UPS-DELFBC is useful to maintain the adequate diversity in the population and avoid the premature convergence during the generational cycle. Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Findings

Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Originality/value

This paper develops a promising UPS-DELFBC approach to solve MOPs. The TDO problems for three different transformer specifications, with 160, 400 and 630 kVA, have been addressed in this paper. Optimization results show the potential and efficiency of the UPS-DELFBC to solve multiobjective TDO and to produce multiple Pareto solutions.

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