A modified choice function hyper-heuristic controlling unary and binary operators

Drake, John H.; Özcan, Ender; Burke, Edmund K.

doi:10.1109/cec.2015.7257315

Cited by 17 publications

(22 citation statements)

References 24 publications

(21 reference statements)

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“…The chosen heuristics logically form a chain of a heuristic sequence as the search progresses. Although there are previous studies ( [9], [48], [57], [58], [59]) using some notion of transition probabilities to keep track of the performance of heuristics invoked successively, none of them employed the same reinforcement learning scheme as we proposed. More importantly, all the previously mentioned algorithms were tested on single objective optimisation problems under a single point based search framework managing move operators rather than metaheuristics.…”

Section: B Reinforcement Learning Schemementioning

confidence: 99%

A Learning Automata-Based Multiobjective Hyper-Heuristic

Özcan

John

2019

IEEE Trans. Evol. Computat.

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Section: B Reinforcement Learning Schemementioning

confidence: 99%

A Learning Automata-Based Multiobjective Hyper-Heuristic

Özcan

John

2019

IEEE Trans. Evol. Computat.

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“…An improved choice function hyper-heuristics is proposed in (Drake et al, 2012) showing that this approach is more successful than the traditional choice function heuristic selection. Drake et al (2015) extended this previous study introducing crossover operators into the set of low level heuristics and a mechanism to control the input those binary operators, which slightly improved the overall performance of the hyper-heuristic. An adaptive iterated local search approach is proposed and applied on HyFlex problem domains in (Burke et al, 2011;Ochoa et al, 2012b).…”

Section: Hyflexmentioning

confidence: 93%

An iterated multi-stage selection hyper-heuristic

Kheiri

Özcan

2016

European Journal of Operational Research

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There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach.

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“…(v) Peckish selection follows the same strategy as the greedy selection, but a subset of low-level heuristics is considered instead of all low-level heuristics (see Chakhlevitch 2003, 2007) and, (vi) Reinforcement Learning heuristic selection is based on a reward value for each heuristic. Such type of reward that represents its historical performance has been considered by Özcan et al (2012), Drake et al (2015), and Aron et al (2015).…”

Section: Hyper-heuristic For Csaphlrpmentioning

confidence: 99%

The capacitated single-allocation p-hub location routing problem: a Lagrangian relaxation and a hyper-heuristic approach

Danach

Gelareh²,

Monemi

2019

EURO Journal on Transportation and Logistics

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A variant of the hub location routing problem studied in this work, which is the problem of locating a set of hub nodes, is establishing the hub-level network and allocating the spoke nodes to the hub nodes. As a particular property of this problem, each cluster of spoke nodes allocated to a hub constitutes a directed route that starts from the hub, visits all the spokes in the same cluster, and terminates to the same hub. We propose a hybrid of hyper-heuristic and a relax-and-cut solution method, which includes cooperation among several low-level heuristics governed and controlled by a learning mechanism. This hybridization provides a mechanism in which the obtained dual information through the Lagrangian relaxation (bundle) method being utilized to guide the local searches for constructing/improving feasible solutions. Several classes of valid inequalities as well as efficient separation routings are also proposed for being used within the relax-and-cut approach. Our extensive computational experiments confirm the efficiency of this solution method in terms of quality as well as computational time.

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A modified choice function hyper-heuristic controlling unary and binary operators

Cited by 17 publications

References 24 publications

A Learning Automata-Based Multiobjective Hyper-Heuristic

A Learning Automata-Based Multiobjective Hyper-Heuristic

An iterated multi-stage selection hyper-heuristic

The capacitated single-allocation p-hub location routing problem: a Lagrangian relaxation and a hyper-heuristic approach

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