Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism

Meignan, David; Koukam, Abderraf́ıâa; Créput, Jean-Charles

doi:10.1007/s10732-009-9121-7

Cited by 42 publications

(33 citation statements)

References 37 publications

(38 reference statements)

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“…Biazzini et al (2009) combine several algorithms for numerical optimisation such as differential evolution and random search in a distributed framework in an island model. Meignan et al (2010) presented a self-adaptive and distributed approach based on agents and hyper-heuristics. Several agents concurrently explore the search space using a set of operators.…”

Section: Blazewicz Et Al (2011) Studied Choice Function Andmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

968

550

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

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Section: Blazewicz Et Al (2011) Studied Choice Function Andmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

968

550

View full text Add to dashboard Cite

show abstract

“…In our early tests, these rather simple agents provided better results than the usage of more sophisticated structures. We also tested an approach based on self-adaptive metaheuristics (as suggested in Meignan, Koukam, and Creput 2010), in which agents consist of multiple small operations and a second-order heuristic algorithm is used to determine which operations should be used and with which intensity. However, this did not yield better results than using the rather unsophisticated agents.…”

Section: An Asynchronous Team Approach For Fine Planningmentioning

confidence: 99%

Two-stage scheduling in shelf-board production: a case study

Waldherr

Knust

2013

International Journal of Production Research

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In this paper, we consider a production planning problem arising at a company assembling shelf boards for kitchen elements. Different products have to be produced on circular production units with a synchronous transportation system, which leads to sequence-dependent production times. Additionally, changeover times occur due to changes of resources. The objective is to find a production schedule of given orders such that the number of late orders is minimised. We propose a hierarchical approach which is split into a coarse and a fine planning stage. While the coarse planning problem is solved as a network flow problem, the fine planning is done by an asynchronous team (A-team) approach. Finally, some computational results for real-world data are presented.

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“…For example, Ozcan et al (2006) classified the LLHs into hill climbers and mutational heuristics; Meignan et al (2010) partitioned the LLH set into an intensifier set and a diversifier set. Another division criterion was proposed in Burke, Curtois, , in which the diversification LLHs were further classified into mutational, ruin-recreate, crossover, and others.…”

Section: Heuristic Space Reduction For Ad-ahmentioning

confidence: 99%

Hyper-Heuristics with Low Level Parameter Adaptation

Zhang

Jiang

Xuan

et al. 2012

Evolutionary Computation

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Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.

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Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism

Cited by 42 publications

References 37 publications

Hyper-heuristics: a survey of the state of the art

Hyper-heuristics: a survey of the state of the art

Two-stage scheduling in shelf-board production: a case study

Hyper-Heuristics with Low Level Parameter Adaptation

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