Channel assignment optimisation using a hyper-heuristic

Communications in Computer and Information Science

Mısır

Özcan

2016

Abstract. Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyperheuristics, deciding whether to accept or reject a new solution at each step during the search process. This study investigates group decision making strategies as ensemble methods exploiting the strengths of multiple move acceptance methods for improved performance. The empirical results indicate the success of the proposed methods across six combinatorial optimisation problems from a benchmark as well as an examination timetabling problem.

Section: Group Decision Making Selection Hyper-heuristicsmentioning

confidence: 99%

Ensemble Move Acceptance in Selection Hyper-heuristics

Communications in Computer and Information Science

Mısır

Özcan

2016

“…The cost value of the first generated candidate solution can be used as the initial level in GD. Great deluge is utilised as a move acceptance method within selection hyper-heuristics in (Kendall and Mohamad, 2004), at which the threshold level (τ ) at time t current is updated with the following equation:…”

Section: Hyper-heuristicsmentioning

confidence: 99%

A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems

Evolutionary Computation

Keedwell

2017

Operations research is a well established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this paper a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state-of-theart.

“…At each step, a random heuristic will be selected and applied to the current solution. Great deluge move acceptance method [9] accepts improving solutions by default. A non-improving solution is only accepted if its quality is better than a threshold level at each iteration.…”

Section: Selection Hyper-heuristics For the Extended Hyflex Problem Dmentioning

confidence: 99%

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Almutairi

Özcan

Communications in Computer and Information Science

et al. 2016

Abstract. Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0-1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the 'unseen' problems in addition to the six standard HyFlex problem domains.