Paper Info
Title
A multi-objective niching co-evolutionary algorithm (MNCA) for identifying diverse sets of non-dominated solutions
Abstract
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 Pareto 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 the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies a set of Pareto-optimal solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of the Pareto front. MNCA is demonstrated for a set of multi-modal multi-objective test problems to identify a set of Pareto fronts with maximum difference in decision vectors.
Year
DOI
Venue
2011
10.1145/2001858.2002098
GECCO (Companion)
Keywords
Field
DocType
engineering design problem,multiple objective,multiple optimum,alternative pareto front,multi-modal multi-objective test problem,co-evolutionary algorithm,diverse set,non-dominated solution,pareto front,decision space,decision vector,optimization problem,problem solution,mathematical model,engineering design,evolutionary algorithm
Mathematical optimization,Evolutionary algorithm,Computer science,Multi-objective optimization,Engineering design process,Artificial intelligence,Optimization problem,Pareto principle,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Emily Michelle Zechman100.34
Marcio H. Giacomoni262.03
M. Ehsan Shafiee3153.41