Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

Yi Chen, Zhonglai Wang, Erfu Yang, Yun Li

Research output: Contribution to conferenceProceeding

7 Citations (Scopus)
214 Downloads (Pure)

Abstract

In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations.
Original languageEnglish
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 15 Dec 2016
Event The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) - Sichuan Province, Chengdu, China
Duration: 15 Dec 201617 Dec 2016
http://fusion-edu.eu/SKIMA2016/

Conference

Conference The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016)
Abbreviated titleSKIMA2016
CountryChina
CityChengdu
Period15/12/1617/12/16
Internet address

Keywords

  • multi-objective optimisation
  • pareto-optimality
  • evolutionary algorithm
  • solution recommendation
  • artificial wolf-pack algorithm
  • decision making

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