Title :
Reference point based multi-objective optimization through decomposition
Author :
Mohammadi, Asad ; Omidvar, Mohammad Nabi ; Li, Xiaodong
Author_Institution :
Sch. of Comput. Sci. & IT, RMIT Univ., Melbourne, VIC, Australia
Abstract :
In this paper we propose a user-preference based evolutionary algorithm that relies on decomposition strategies to convert a multi-objective problem into a set of single-objective problems. The use of a reference point allows the algorithm to focus the search on more preferred regions which can potentially save considerable amount of computational resources. The algorithm that we proposed, dynamically adapts the weight vectors and is able to converge close to the preferred regions. Combining decomposition strategies with reference point approaches paves the way for more effective optimization of many-objective problems. The use of a decomposition method alleviates the selection pressure problem associated with dominance-based approaches while a reference point allows a more focused search. The experimental results show that the proposed algorithm is capable of finding solutions close to the reference points specified by a decision maker. Moreover, our results show that high quality solutions can be obtained using less computational effort as compared to a state-of-the-art decomposition based evolutionary multi-objective algorithm.
Keywords :
evolutionary computation; decomposition strategies; dominance-based approach; multiobjective optimization; reference point; selection pressure problem; user-preference based evolutionary algorithm; Vectors;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256486