Abstract
Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto archived evolution strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm is identified as being a (1+1) evolution strategy, using local search from a population of one but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. The performance of the new algorithm is compared with that of a MOEA based on the niched Pareto GA on a real world application from the telecommunications field. In addition, we include results from experiments carried out on a suite of four test functions, to demonstrate the algorithm's general capability.
Keywords
Pareto principleMathematical optimizationEvolutionary algorithmPopulationComputer scienceMulti-objective optimizationTest suiteBaseline (sea)Ranking (information retrieval)Set (abstract data type)AlgorithmPareto efficiencyMathematicsTest caseArtificial intelligenceMachine learning
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Publication Info
- Year
- 2003
- Type
- article
- Pages
- 98-105
- Citations
- 1267
- Access
- Closed
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Cite This
Joshua Knowles,
David Corne
(2003).
The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation.
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
, 98-105.
https://doi.org/10.1109/cec.1999.781913
Identifiers
- DOI
- 10.1109/cec.1999.781913