Performance measures for dynamic multi-objective optimisation algorithms

Helbig, Mardé; Engelbrecht, Andries P.

doi:10.1016/j.ins.2013.06.051

Cited by 96 publications

(40 citation statements)

References 49 publications

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“…In order to evaluate the performance the hypervolume indicator has been chosen by following the indications in [31,83]. The hypervolume indicator is selected because it is scaling independent and requires no prior knowledge of the true Pareto-optimal front, this is important when working with real-world problems which have not yet been solved.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The way the hypervolume indicator is constructed is very appealing in real-world problems as it requires no information regarding the theoretical Pareto-front (which is often unknown in practise.). Furthermore, the hypervolume indicator encompasses within it the information about proximity, diversity, and pertinence, ultimately evaluating the quality of the approximation set, see [31]. Successful examples of incorporating the hypervolume indicator into the optimisation process during the selection stage can be found in [22] and [3], where an adapted version of the hypervolume indicator, named the contributing hypervolume indicator, is used.…”

Section: Introductionmentioning

confidence: 99%

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Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Rostami

Neri

2016

ICA

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Abstract. Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation.The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Rostami

Neri

2016

ICA

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“…Furthermore, to evaluate the diversity of the proposed algorithm, two diversity indicators are used to compare the spread of the solutions found between NSGA-II-LS, NSGA-II and MODEA [33]. The first diversity performance measures is spacing metric of Schott (S) which measures how evenly the points of approximated Pareto front are distributed in the objective space.…”

Section: Performance Measuresmentioning

confidence: 99%

Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search

Nguyen

Passow

Yang

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability to return as good solutions as possible at any time during its implementation. Therefore, anytime behavior is desirable in traffic signal timing optimisation algorithms. In this study, we propose an optimisation strategy (NSGA-II-LS) to improve anytime behaviour based on NSGA-II and local search. To evaluate the validity of the proposed algorithm, the NSGA-II-LS, NSGA-II and MODEA are used to optimize signal durations of an intersection in Andrea Costa scenario. Results of the experiment show that the optimization method proposed in this study has good anytime behaviour in the traffic signal timings optimization problem.

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“…The hypervolume indicator is selected because it is scaling independent and requires no prior knowledge of the true Pareto front, this is important when working with real-world problems which have not yet been solved. The hypervolume indicator is currently used in the field of multi-objective optimisation as both a performance metric and in the decision making process [21,33].…”

Section: Performance Assessmentmentioning

confidence: 99%

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

Rostami

O'Reilly

Shenfield

et al. 2015

Information Sciences

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This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published versionROSTAMI, Shahin, O'REILLY, Dean, SHENFIELD, Alex and BOWRING, Nicholas (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520. Copyright and re-use policy AbstractThe incorporation of decision maker preferences is often neglected in the Evolutionary MultiObjective Optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the Multi-Objective Evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of bi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for Concealed Weapon Detection, producing better results than previously published classifier implementations.

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Performance measures for dynamic multi-objective optimisation algorithms

Cited by 96 publications

References 49 publications

Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

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