A critical survey of performance indices for multi-objective optimisation

Okabe, Tatsuya; Jin, Yaochu; Sendhoff, Bernhard

doi:10.1109/cec.2003.1299759

Cited by 184 publications

(129 citation statements)

References 21 publications

(15 reference statements)

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“…This ensures that P true contains all of the D paths computed by Dijstra's algorithm on each of the D criteria and other pareto optimal solutions generated by the EA and the k-shortest path algorithm. The approach we use to generating P true is similar to that used in Okabe et al (2003) and Baran et al (2001) For a more indepth coverage of quality metrics for multicriteria optimization the work by Van Veldhuizen and Lamont (2000) provides examples of a wide range of metrics.…”

Section: Assessing the Quality Of The Ea Approachmentioning

confidence: 99%

An evolutionary algorithm for multicriteria path optimization problems

Mooney

Winstanley

2006

International Journal of Geographical Information Science

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For many years researchers and decision makers (DMs) faced with multicriteria shortest path problems (MSPPs) have resorted to reductions to the classical shortest path problem (SPP) by means of weighted linear combinations of the criteria. Algorithmic and approximation schemes are available to solve MSPPs but these approaches often display complexities prohibitive to their implementation on real-world applications. This paper describes the development of an Evolutionary Algorithm (EA) approach to MSPPs on networks with multiple independent criteria. The EA approach is shown to sufficiently explore the underlying network space, generate large candidate path sets, and evolve high quality approximations to the optimal MSPP solution(s). Opportunities for early termination of the EA in time-critical applications are also offered. Among the issues for further work is the integration of the EA as a tool within a GIS for path optimization.

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Section: Assessing the Quality Of The Ea Approachmentioning

confidence: 99%

An evolutionary algorithm for multicriteria path optimization problems

Mooney

Winstanley

2006

International Journal of Geographical Information Science

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“…In the literature, many performance indicators have been proposed [12,17] and classified according to several properties: (i) cardinality, (ii) convergence and (iii) distribution. It has also been shown that it is generally not possible to aggregate such properties into a single indicator.…”

Section: Preliminariesmentioning

confidence: 99%

Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation

Blot

Pernet

Jourdan

et al. 2017

Lecture Notes in Computer Science

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Abstract. Automatic algorithm configuration (AAC) is becoming an increasingly crucial component in the design of high-performance solvers for many challenging combinatorial optimisation problems. This raises the question how to most effectively leverage AAC in the context of building or optimising multi-objective optimisation algorithms, and specifically, multi-objective local search procedures. Because the performance of multi-objective optimisation algorithms cannot be fully characterised by a single performance indicator, we believe that AAC for multi-objective local search should make use of multi-objective configuration procedures. We test this belief by using MO-ParamILS to automatically configure a highly parametric iterated local search framework for the classical and widely studied bi-objective permutation flowshop problem. To the best of our knowledge, this is the first time a multi-objective optimisation algorithm is automatically configured in a multi-objective fashion, and our results demonstrate that this approach can produce very good results as well as interesting insights into the efficacy of various strategies and components of a flexible multi-objective local search framework.

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“…Different performance measures for evaluating efficiency have been suggested in literature (Okabe et al, 2003). For comparison purpose, this study applies two metrics: 1) the convergence metric (Υ): approximating the average distance to the Pareto-optimal front; and 2) the diversity metric (Δ): measuring the extent of spread achieved among the obtained solutions (Deb, 2001).…”

Section: Performance Measuresmentioning

confidence: 99%