A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization

dc.cclicenceCC-BY-NCen
dc.contributor.authorJiang, Shouyongen
dc.contributor.authorYang, Shengxiangen
dc.date.acceptance2016-06-27en
dc.date.accessioned2016-09-21T10:36:36Z
dc.date.available2016-09-21T10:36:36Z
dc.date.issued2017-03-24
dc.descriptionOpen Accessen
dc.description.abstractWhile Pareto-based multi-objective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been gradually recognized. In this paper, we revive an early-developed and computationally expensive strength Pareto based evolutionary algorithm by introducing an efficient reference direction based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multi- and many-objective problems. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed method shows very competitive performance on both multi- and many-objective problems considered in this study. Besides, our extensive investigations and discussions reveal an interesting finding, that is, diversity-first-and-convergence-second selection strategies may have great potential to deal with many-objective optimization.en
dc.funderEngineering and Physical Sciences Research Council (EPSRC)en
dc.identifier.citationJiang, S. and Yang, S. (2016) A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary Computation, 21 (3), pp. 329-346en
dc.identifier.doihttps://doi.org/10.1109/TEVC.2016.2592479
dc.identifier.urihttp://hdl.handle.net/2086/12627
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidEP/K001310/1en
dc.publisherIEEE Pressen
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectMulti-objective optimizationen
dc.subjectmany-objective optimizationen
dc.subjectstrength Pareto evolutionary algorithmen
dc.subjectreference directionen
dc.subjectcomputational complexityen
dc.titleA strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimizationen
dc.typeArticleen

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
IEEETEVC16-All.pdf
Size:
4.98 MB
Format:
Adobe Portable Document Format
Description:
Main article
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.2 KB
Format:
Item-specific license agreed upon to submission
Description: