A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing

Chow, Joseph Y.J.; Regan, Amelia

doi:10.1007/s11081-013-9227-5

Cited by 28 publications

(13 citation statements)

References 54 publications

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“…All settings for the test suit are fixed using the same settings proposed in the previous studies (Zitzler et al, 2000;Huband et al, 2006). According to (Voutchkov & Keane, 2010;Chow & Regan, 2012) an algorithm could reach better convergence by 6,250 generations. Therefore, the HH CF was terminated after 6,250 generations.…”

Section: Experimental Settings and Performance Evaluation Criteriamentioning

confidence: 99%

A multi-objective hyper-heuristic based on choice function

Maashi

Özcan

Kendall

2014

Expert Systems with Applications

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Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.

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Section: Experimental Settings and Performance Evaluation Criteriamentioning

confidence: 99%

A multi-objective hyper-heuristic based on choice function

Maashi

Özcan

Kendall

2014

Expert Systems with Applications

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“…Following the recommendation in [63] and [64], a hyper-heuristic is terminated after 6,250 generations. All hyper-heuristics are run for a total of 25 stages.…”

Section: Experimental Settingsmentioning

confidence: 99%

Choice function based hyper-heuristics for multi-objective optimization

Maashi

Kendall

Özcan

2015

Applied Soft Computing

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A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as nondeterministic move acceptance methods for multi-objective optimization. A well known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the nona Dr Maashi (m.maashi@gmail.com) works as an Assistant Professor at the Department of Computer Science, Faculty of Computer and Information Technology, University of Tabuk, Saudi Arabia. Preprint submitted to Applied Soft ComputingOctober 8, 2014 deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.

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“…It is quite common to consider a multiobjective function in NDPs (Chow and Regan, 2012;Xie, 2014) and TRNDPs (see for example Farahani et al, 2013;Guihaire and Hao, 2008). Decision variables, definition of the objective function, and solution methods have attracted most of the research, focusing on TRNDP while demand elasticity has been less studied (Kepaptsoglou and Karlaftis, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

A Clustering Algorithm for Bi‐Criteria Stop Location Design with Elastic Demand

Rashidi

Rey

Jian

et al. 2015

Computer aided Civil Eng

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This article proposes a bi‐criteria formulation to find the optimal location of light rapid transit stations in a network where demand is elastic and budget is constrained. Our model is composed of two competing objective functions seeking to maximize the total ridership and minimize the total budget allocated. In this research, demand is formulated using the random utility maximization method with variables including access time and travel time. The transit station location problem of this study is formulated using mixed integer programming and we propose a heuristic solution algorithm to solve large‐scale instances which is inspired by the problem context. The elastic demand is integrated with the optimization problem in an innovative way which facilitates the solution process. The performance of our model is evaluated on two test problems and we carry out its implementation on a real‐world instance. Due to the special shape of the Pareto front function, significant practical policy implications, in particular budget allocation, are discussed to emphasize the fact that the trade‐off between cost and benefit may result in large investments with little outcomes and vice versa.

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A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing

Cited by 28 publications

References 54 publications

A multi-objective hyper-heuristic based on choice function

A multi-objective hyper-heuristic based on choice function

Choice function based hyper-heuristics for multi-objective optimization

A Clustering Algorithm for Bi‐Criteria Stop Location Design with Elastic Demand

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