ORCID Profile
0000-0003-0682-1363
Current Organisation
Victoria University of Wellington
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Publisher: IEEE
Date: 05-2015
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 06-07-2018
Publisher: ACM
Date: 20-07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 05-2015
Publisher: Springer Science and Business Media LLC
Date: 17-10-2014
DOI: 10.1007/S00500-014-1487-2
Abstract: In this paper, the interdependence between sub-problems in a complex overall problem is investigated using a benchmark problem called Travelling Thief Problem (TTP), which is a combination of Travelling Salesman Problem (TSP) and Knapsack Problem (KP). First, the analysis on the mathematical formulation shows that it is impossible to decompose the problem into independent sub-problems due to the non-linear relationship in the objective function. Therefore, the algorithm for TTP is not straightforward although each sub-problem alone has been investigated intensively. Then, two meta-heuristics are proposed for TTP. One is the Cooperative Co-evolution (CC) that solves the sub-problems separately and transfers the information between them in each generation. The other is the Memetic Algorithm (MA) that solves TTP as a whole. The comparative results showed that MA consistently obtained much better results than both the standard and dynamic versions of CC within comparable computational budget. This indicates the importance of considering the interdependence between sub-problems in an overall problem like TTP.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 18-04-2017
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 15-07-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 18-12-2020
Publisher: ACM
Date: 12-07-2023
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2016
Publisher: ACM
Date: 12-07-2023
Publisher: ACM
Date: 12-07-2023
Publisher: IEEE
Date: 18-07-2022
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer International Publishing
Date: 2018
Publisher: MIT Press
Date: 09-2019
DOI: 10.1162/EVCO_A_00230
Abstract: Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.
Publisher: IEEE
Date: 12-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 12-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 24-11-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2011
Publisher: ACM
Date: 07-2017
Publisher: IEEE
Date: 28-06-2021
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 12-2019
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE Comput. Soc
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
Publisher: IEEE
Date: 28-06-2021
Publisher: MIT Press - Journals
Date: 06-2020
DOI: 10.1162/EVCO_A_00256
Abstract: The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality and robust baseline task sequence, and the other is to design an effective recourse policy to adjust the baseline task sequence when it becomes infeasible and/or ineffective during the execution. Existing studies typically only tackle one challenge (the other being addressed using a naive strategy). No existing work optimises the baseline task sequence and recourse policy simultaneously. To fill this gap, we propose a novel proactive-reactive approach, which represents a solution as a baseline task sequence and a recourse policy. The two components are optimised under a cooperative coevolution framework, in which the baseline task sequence is evolved by an estimation of distribution algorithm, and the recourse policy is evolved by genetic programming. The experimental results show that the proposed algorithm, called Solution-Policy Coevolver, significantly outperforms the state-of-the-art algorithms to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Through further analysis, we discovered that route failure is not always detrimental. Instead, in certain cases (e.g., when the vehicle is on the way back to the depot) allowing route failure can lead to better solutions.
Publisher: ACM
Date: 06-07-2018
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 13-07-2019
Publisher: arXiv
Date: 2019
Publisher: ACM
Date: 07-2017
Publisher: IEEE
Date: 06-2013
Publisher: ACM
Date: 13-07-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 27-12-2016
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 27-12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Elsevier BV
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: ACM
Date: 07-2017
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: MIT Press - Journals
Date: 03-2021
DOI: 10.1162/EVCO_A_00273
Abstract: Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 26-06-2021
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 12-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Informa UK Limited
Date: 15-09-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: MIT Press - Journals
Date: 12-2020
DOI: 10.1162/EVCO_A_00267
Abstract: Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity ( route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: ACM
Date: 09-07-2022
Publisher: IEEE
Date: 05-12-2021
Publisher: IEEE
Date: 05-12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 28-06-2021
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 12-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 05-12-2021
Publisher: ACM
Date: 08-07-2020
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 07-2010
Publisher: ACM
Date: 13-07-2019
Publisher: IEEE
Date: 05-12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 15-07-2023
Publisher: IEEE
Date: 28-06-2021
Publisher: Elsevier BV
Date: 04-2022
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 11-07-2015
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 07-2016
Publisher: Springer Science and Business Media LLC
Date: 05-04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 11-2011
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2017
Publisher: ACM
Date: 15-07-2023
Publisher: IEEE
Date: 12-2020
Publisher: Informa UK Limited
Date: 03-04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: IEEE
Date: 18-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: IEEE
Date: 18-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 28-06-2021
Publisher: IEEE
Date: 28-06-2021
Publisher: IEEE
Date: 28-06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Springer Science and Business Media LLC
Date: 30-09-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 18-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: ACM
Date: 08-07-2022
Publisher: ACM
Date: 08-07-2022
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 18-07-2022
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 06-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 11-2003
Publisher: ACM
Date: 15-07-2023
Publisher: Springer International Publishing
Date: 09-11-2016
Publisher: IJCIEOM 2020 - International Joint Conference on Industrial Engineering and Operations Management
Date: 14-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 08-07-2022
Publisher: Springer Singapore
Date: 2021
Publisher: ACM
Date: 20-07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 07-2020
Publisher: ACM
Date: 20-07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Springer Science and Business Media LLC
Date: 24-02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Elsevier BV
Date: 04-2017
Publisher: IEEE
Date: 06-2017
Publisher: ACM
Date: 26-06-2021
Publisher: ACM
Date: 08-07-2022
Publisher: IEEE
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 05-12-2021
Publisher: IEEE
Date: 18-07-2022
Publisher: ACM
Date: 09-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2011
Publisher: Springer International Publishing
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 03-06-2016
DOI: 10.1145/2791291
Abstract: This article proposes a competitive ide-and-conquer algorithm for solving large-scale black-box optimization problems for which there are thousands of decision variables and the algebraic models of the problems are unavailable. We focus on problems that are partially additively separable, since this type of problem can be further decomposed into a number of smaller independent subproblems. The proposed algorithm addresses two important issues in solving large-scale black-box optimization: (1) the identification of the independent subproblems without explicitly knowing the formula of the objective function and (2) the optimization of the identified black-box subproblems. First, a Global Differential Grouping (GDG) method is proposed to identify the independent subproblems. Then, a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is adopted to solve the subproblems resulting from its rotation invariance property. GDG and CMA-ES work together under the cooperative co-evolution framework. The resultant algorithm, named CC-GDG-CMAES, is then evaluated on the CEC’2010 large-scale global optimization (LSGO) benchmark functions, which have a thousand decision variables and black-box objective functions. The experimental results show that, on most test functions evaluated in this study, GDG manages to obtain an ideal partition of the index set of the decision variables, and CC-GDG-CMAES outperforms the state-of-the-art results. Moreover, the competitive performance of the well-known CMA-ES is extended from low-dimensional to high-dimensional black-box problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Elsevier BV
Date: 03-2017
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 18-07-2022
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 25-06-2020
Publisher: IEEE
Date: 07-2014
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 07-2010
Start Date: 2017
End Date: 2020
Funder: Marsden Fund
View Funded ActivityStart Date: 2023
End Date: 2026
Funder: Ministry of Business, Innovation and Employment
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