Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 민배현 | * |
dc.date.accessioned | 2019-11-19T16:31:03Z | - |
dc.date.available | 2019-11-19T16:31:03Z | - |
dc.date.issued | 2015 | * |
dc.identifier.issn | 1568-4946 | * |
dc.identifier.issn | 1872-9681 | * |
dc.identifier.other | OAK-25788 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/252079 | - |
dc.description.abstract | This study investigates the coupling effects of objective-reduction and preference-ordering schemes on the search efficiency in the evolutionary process of multi-objective optimization. The difficulty in solving a many-objective problem increases with the number of conflicting objectives. Degenerated objective space can enhance the multi-directional search toward the multi-dimensional Pareto-optimal front by eliminating redundant objectives, but it is difficult to capture the true Pareto-relation among objectives in the non-optimal solution domain. Successive linear objective-reduction for the dimensionality-reduction and dynamic goal programming for preference-ordering are developed individually and combined with a multi-objective genetic algorithm in order to reflect the aspiration levels for the essential objectives adaptively during optimization. The performance of the proposed framework is demonstrated in redundant and non-redundant benchmark test problems. The preference-ordering approach induces the non-dominated solutions near the front despite enduring a small loss in diversity of the solutions. The induced solutions facilitate a degeneration of the Pareto-optimal front using successive linear objective-reduction, which updates the set of essential objectives by excluding non-conflicting objectives from the set of total objectives based on a principal component analysis. Salient issues related to real-world problems are discussed based on the results of an oil-field application. (C) 2015 Elsevier B.V. All rights reserved. | * |
dc.language | English | * |
dc.publisher | ELSEVIER | * |
dc.subject | Objective-reduction | * |
dc.subject | Preference-ordering | * |
dc.subject | Evolutionary process | * |
dc.subject | Many-objective problem | * |
dc.subject | Pareto-optimal front | * |
dc.title | Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction | * |
dc.type | Article | * |
dc.relation.volume | 35 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 75 | * |
dc.relation.lastpage | 112 | * |
dc.relation.journaltitle | APPLIED SOFT COMPUTING | * |
dc.identifier.doi | 10.1016/j.asoc.2015.06.007 | * |
dc.identifier.wosid | WOS:000360109900007 | * |
dc.author.google | Min, Baehyun | * |
dc.author.google | Park, Changhyup | * |
dc.author.google | Jang, Ilsik | * |
dc.author.google | Kang, Joe M. | * |
dc.author.google | Chung, Sunghoon | * |
dc.contributor.scopusid | 민배현(45961384800) | * |
dc.date.modifydate | 20240322114211 | * |