Paper Info
Title
Use of cooperative coevolution for solving large scale multiobjective optimization problems.
Abstract
Many real-world multi-objective optimization problems have hundreds or even thousands of decision variables, which contrast with the current practice of multi-objective metaheuristics whose performance is typically assessed using benchmark problems with a relatively low number of decision variables (normally, no more than 30). In this paper, we propose a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems. We adopt a benchmark that is scalable in the number of decision variables (the ZDT test suite) and compare our proposed algorithm with respect to two state-of-the-art multi-objective evolutionary algorithms (GDE3 and NSGA-II) when using a large number of decision variables (from 200 up to 5000). The results clearly indicate that our proposed approach is effective as well as efficient for solving large scale multi-objective optimization problems.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557903
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
Pareto optimisation,genetic algorithms,GDE3 algorithm,NSGA-II algorithm,cooperative coevolution framework,decision variable,large scale multiobjective optimization,multiobjective metaheuristics,nondominated sorting genetic algorithm
Test suite,Mathematical optimization,Evolutionary algorithm,Computer science,Cooperative coevolution,Multi-objective optimization,Artificial intelligence,Optimization problem,Genetic algorithm,Machine learning,Metaheuristic,Scalability
Conference
Citations 
PageRank 
References 
39
0.95
12
Authors
2
Name
Order
Citations
PageRank
Luis Miguel Antonio1583.92
C. A. Coello Coello25799427.99