Paper Info
Title
Improving Hypervolume-Based Multiobjective Evolutionary Algorithms By Using Objective Reduction Methods
Abstract
Hypervolume based multiobjective evolutionary algorithms (MOEA) nowadays seem to be the first choice when handling multiobjective optimization problems with many, i.e., at least three objectives. Experimental studies have shown that hypervolume-based search algorithms as SMS-EMOA can outperform established algorithms like NSGA-II and SPEA2. One problem remains with most of the hypervolume based algorithms: the best known algorithm for computing the hypervolume needs time exponentially in the number of objectives. To save computation time during hypervolume computation which can be better spent in the generation of more solutions, we propose a general approach how objective reduction techniques can be incorporated into hypervolume based algorithms. Different objective reduction strategies are developed and then compared in an experimental study on two test problems with up to nine objectives. The study indicates that the (temporary) omission of objectives can improve hypervolume based MOEAs drastically in terms of the achieved hypervolume indicator values.
Year
DOI
Venue
2007
10.1109/CEC.2007.4424730
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS
Keywords
Field
DocType
search algorithm,multiobjective optimization,evolutionary computation
Mathematical optimization,Search algorithm,Evolutionary algorithm,Computer science,Evolutionary computation,Artificial intelligence,Multiobjective optimization problem,Machine learning,Computation
Conference
Citations 
PageRank 
References 
56
2.43
14
Authors
2
Name
Order
Citations
PageRank
Dimo Brockhoff194853.97
Eckart Zitzler24678291.01