Multi-objective Optimisation Based on Relation Favour

Abstract. The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We incorporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.

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“…Three that are considered in this study are hypervolume [8], the favour relation [5], and an indicator based on the average rank method [1].…”

Section: Indicatorsmentioning

confidence: 99%

“…Given two solutions y i and y j , the favour relation [5] determines which is the fitter solution in terms of which is dominant on the most objectives. More formally:…”

Section: Favour Relationmentioning

confidence: 99%

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

Walker

Keedwell

2016

Parallel Problem Solving From Nature – PPSN XIV

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“…The Favour Relation (FR) [9] uses partial dominance to build a directed 'relation' graph used to rank solutions.…”

Section: Favourmentioning

confidence: 99%

“…In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain [1,6,8,9,20] when used within PSO. As such, we limit the variation of the baseline optimiser used to a simple PSO, and vary purely the use of these measures for archive maintenance and for selection.…”

Section: Introductionmentioning

confidence: 99%

On the effect of selection and archiving operators in many-objective particle swarm optimisation

Woolard

Fieldsend

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multi-objective optimisation, however its performance on many-objective optimisation (problems with four or more competing objectives) has been less well examined. Many-objective optimisation is well-known to cause problems for Pareto-based evolutionary optimisers, so it is of interest to see how well PSO copes in this domain, and how non-Pareto quality measures perform when integrated into PSO. Here we compare and contrast the performance of canonical PSO, using a wide range of many-objective quality measures, on a number of different parametrised test functions for up to 20 competing objectives. We examine the use of eight quality measures as selection operators for guides when truncated nondominated archives of guides are maintained, and as maintenance operators, for choosing which solutions should be maintained as guides from one generation to the next. We find that the Controlling Dominance Area of Solutions approach performs exceptionally well as a quality measure to determine archive membership for global and local guides. As a selection operator, the Average Rank and Sum of Ratios measures are found to generally provide the best performance.

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“…This was an extension of the original Favour relation introduced in [7]. The Favour relation is a relaxed version of dominance.…”

Section: Related Workmentioning

confidence: 99%

Using a distance metric to guide PSO algorithms for many-objective optimization

Wickramasinghe

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for manyobjective problems. We use a particle swarm optimization (PSO) algorithm as a baseline to demonstrate the usefulness of this distance metric, though the metric can be used in conjunction with any evolutionary multi-objective (EMO) algorithm. Existing user-preference based EMO algorithms rely on the use of dominance comparisons to explore the searchspace. Unfortunately, this is ineffective and computationally expensive for many-objective problems. In the proposed distance metric based PSO, particles update their positions and velocities according to their closeness to preferred regions in the objective-space, as specified by the decision maker. The proposed distance metric allows an EMO algorithm's search to be more effective especially for many-objective problems, and to be more focused on the preferred regions, saving substantial computational cost. We demonstrate how to use a distance metric with two user-preference based PSO algorithms, which implement the reference point and light beam search methods. These algorithms are compared to a userpreference based PSO algorithm relying on the conventional dominance comparisons. Experimental results suggest that the distance metric based algorithms are more effective and efficient especially for difficult many-objective problems.

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Multi-objective Optimisation Based on Relation Favour

Cited by 70 publications

References 11 publications

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

On the effect of selection and archiving operators in many-objective particle swarm optimisation

Using a distance metric to guide PSO algorithms for many-objective optimization

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