A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Jiang, Shouyong | en |
| dc.contributor.author | Yang, Shengxiang | en |
| dc.date.acceptance | 2016-06-27 | en |
| dc.date.accessioned | 2016-09-21T10:36:36Z | |
| dc.date.available | 2016-09-21T10:36:36Z | |
| dc.date.issued | 2017-03-24 | |
| dc.description | Open Access | en |
| dc.description.abstract | While 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.funder | Engineering and Physical Sciences Research Council (EPSRC) | en |
| dc.identifier.citation | Jiang, 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. 329-346 | en |
| dc.identifier.doi | https://doi.org/10.1109/TEVC.2016.2592479 | |
| dc.identifier.uri | http://hdl.handle.net/2086/12627 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | EP/K001310/1 | en |
| dc.publisher | IEEE Press | en |
| dc.researchgroup | Centre for Computational Intelligence | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Multi-objective optimization | en |
| dc.subject | many-objective optimization | en |
| dc.subject | strength Pareto evolutionary algorithm | en |
| dc.subject | reference direction | en |
| dc.subject | computational complexity | en |
| dc.title | A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization | en |
| dc.type | Article | en |