Paper Info
Title
The multiobjective evolutionary algorithm based on determined weight and sub-regional search
Abstract
By dividing the multiobjective optimization of the decision space into several small regions, this paper proposes multi-objective optimization algorithm based on sub-regional search, which makes individuals in same region operate each other by evolutionary operator and the information between the individuals of different regions exchange through their offsprings re-divided into regions again. Since the proposed algorithm utilizes the sub-regional search, the computational complexity at each generation is lower than the NSGA-II and MSEA. The proposed algorithm makes use of the max-min strategy with determined weight as fitness functions, which make it approach evenly distributed solution in Pareto front. This paper presents a kind of easy technology dealing with the constraint, which makes the proposed algorithm solved unconstrained multiobjective problems can also be used to solve constrained multiobjective problems. The numerical results, with 13 unconstrained multiobjective optimization testing instances and 10 constrained multiobjective optimization testing instances, are shown in this paper.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983176
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
fitness function,unconstrained multiobjective optimization testing,evolutionary computation,multi-objective optimization algorithm,sub-regional search,search problems,minimax techniques,multiobjective problem,computational complexity,multiobjective evolutionary algorithm,pareto optimisation,subregional search,determined weight,different regions exchange,proposed algorithm,max-min strategy,pareto front,decision space,multiobjective optimization,multiobjective optimization testing instance,data mining,helium,constraint optimization,convergence,optimization,computational modeling,computer simulation,multi objective optimization,testing,objective function,pediatrics,management science,science and technology,algorithm design and analysis
Mathematical optimization,Algorithm design,Division (mathematics),Evolutionary algorithm,Computer science,Evolutionary computation,Multi-objective optimization,Fitness function,Operator (computer programming),Artificial intelligence,Machine learning,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
978-1-4244-2959-2
44
1.77
References 
Authors
13
2
Name
Order
Citations
PageRank
Hai-lin Liu166852.80
Xueqiang Li2474.54