Authors: Wei Zhan; Wenling You; Ming Zhang
Addresses: School of Computer Science, Yangtze University, Jingzhou, Hubei, China ' School of Computer Science, Yangtze University, Jingzhou, Hubei, China ' School of Computer Science, Yangtze University, Jingzhou, Hubei, China
Abstract: Based on the following property: under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimisation problems (MOPs) is a piecewise continuous (m − 1) − D manifold (where m is the number of objectives), a hybrid multiobjective evolutionary algorithm model based on local linear embedding is proposed for continuous MOPs. At each generation: 1) via local linear embedding and its improved algorithms, the proposed algorithm digs out a nonlinear manifold in the decision space; 2) the new trial solutions are built through the manifold of step 1; 3) a non-dominated sorting-based selection is used for choosing solutions and produce the next generation. Systematic experiments have shown that the algorithm can find out nonlinear manifold hidden in the decision space of MOPs and guide rapid convergence of algorithm.
Keywords: multiobjective optimisation; evolutionary algorithms; local linear embedding; LLE; nonlinear manifold; decision space; NSGA-II; genetic algorithms.
International Journal of Computing Science and Mathematics, 2015 Vol.6 No.3, pp.211 - 220
Available online: 28 May 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article