期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 26, 期 5-6, 页码 1442-1457出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2013.03.004
关键词
Evolutionary computation; Engineering design; Multi-objective optimization; Niching; Alternative generation
Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative non-dominated fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents a new algorithm, the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies distinct sets of non-dominated solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of a Pareto front. MNCA is demonstrated to identify a set of non-dominated fronts with maximum difference in decision vectors for a set of real-valued problems. (C) 2013 Elsevier Ltd. All rights reserved.
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