A comprehensive analysis of hyper-heuristics

Özcan, Ender; Bi̇lgi̇n, Burak; Korkmaz, Emin Erkan

doi:10.3233/ida-2008-12102

Cited by 202 publications

(84 citation statements)

References 22 publications

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“…There is already empirical evidence showing that different combinations of hyper-heuristic components yield different performances (Bilgin et al, 2006;Ö zcan et al, 2008) for solving discrete optimization problems. This study extends our previous work in Kiraz et al (2011) further and provides a complete empirical analysis of different hyperheuristics coupling well-known heuristic selection and move acceptance methods in dynamic environments.…”

Section: ö Zcan Et Almentioning

confidence: 99%

Selection hyper-heuristics in dynamic environments

Kiraz

Etaner-Uyar

Özcan

2013

Journal of the Operational Research Society

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Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real-world problems are dynamic in nature, where different types of changes may occur over time. Population-based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems. Selection hyper-heuristics are highly adaptive search methodologies that aim to raise the level of generality by providing solutions to a diverse set of problems having different characteristics. In this study, the performances of 35 single-point-search-based selection hyper-heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, produced by the Moving Peaks Benchmark generator. Even though there are many successful applications of selection hyper-heuristics to discrete optimization problems, to the best of our knowledge, this study is one of the initial applications of selection hyper-heuristics to real-valued optimization as well as being among the very few which address dynamic optimization issues using these techniques. The empirical results indicate that learning selection hyper-heuristics incorporating compatible components can react to different types of changes in the environment and are capable of tracking them. This study shows the suitability of selection hyper-heuristics as solvers in dynamic environments.

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Section: ö Zcan Et Almentioning

confidence: 99%

Selection hyper-heuristics in dynamic environments

Kiraz

Etaner-Uyar

Özcan

2013

Journal of the Operational Research Society

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“…Secondly, local search algorithms can also be incorporated into the set of available low-level heuristics [18]. This option can be considered an intervention in heuristic space diversity, especially when metaheuristics are utilized as low-level heuristics, since a more diverse set of algorithms are made available to the high-level strategy.…”

Section: B Local Search and Hyperheuristicsmentioning

confidence: 99%

Solution Space Diversity Management in a Meta-hyperheuristic Framework

Grobler

Engelbrecht

2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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This paper investigates various strategies for the management of solution space diversity within the context of a meta-hyperheuristic algorithm. The adaptive local search meta-hyperheuristic (ALSHH), which adaptively applies a local search algorithm when the population diversity strays outside a predetermined solution space diversity profile, is proposed. ALSHH was shown to compare favourably with algorithms making use of local search and diversity maintenance strategies applied at constant intervals throughout the optimization run. Good performance is also demonstrated with respect to two other popular multi-method algorithms.

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“…Thus, the latter approaches make use of a feedback from an ongoing search process. In [1] and [4] surveys of hyperheuristic approaches are presented.…”

Section: State-of-the-artmentioning

confidence: 99%

A domain-inspired hyperheuristic for solving complex design problems of automation systems

Oezluek

Kabitzsch

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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Automation systems comprise often huge and complex communication networks which save resources such as energy, time, capital and effort for humans. Design of automation systems is often a highly complex problem. For creation of optimal building automation system design, if component variety on the market is considered, there emerge many trade-off design solutions. In our previous work high quality design solutions could be obtained by proposed problem-specific adaptations of multi-objective metaheuristics. In this paper, we propose a new domaininspired hyperheuristic approach to obtain the majority of optimal solutions in the true Pareto front that concerns evolving variation applications and selection operations.

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A comprehensive analysis of hyper-heuristics

Cited by 202 publications

References 22 publications

Selection hyper-heuristics in dynamic environments

Selection hyper-heuristics in dynamic environments

Solution Space Diversity Management in a Meta-hyperheuristic Framework

A domain-inspired hyperheuristic for solving complex design problems of automation systems

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