title: A memetic optimization strategy based on dimension reduction in decision space.
abstract: There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.
authors: Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, Xidian University, Xi'an, 710071, China wanghanding@163.com.
authors: Handing
authors: H
authors: Wang
authors: Licheng
authors: L
authors: Jiao
authors: Ronghua
authors: R
authors: Shang
authors: Shan
authors: S
authors: He
authors: Fang
authors: F
authors: Liu
mesh_terms: D000465:Algorithms; D003658:Decision Making, Computer-Assisted; D008962:Models, Theoretical; D057386:Search Engine
keywords: Multi-objective optimization; dimension reduction; evolutionary algorithm; local search; memetic algorithm; portability