Show simple item record

dc.contributor.authorLi, Miqingen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorLi, Keen
dc.contributor.authorLiu, Xiaohuien
dc.identifier.citationLi, M., Yang, S., Li, K. and Liu, X. (2013) Evolutionary algorithms with segment-based search for multiobjective optimization problems. IEEE Transactions on Cybernetics, published online first: 10 October 2013.en
dc.description.abstractThis paper proposes a variation operator, called segment-based search (SBS), to improve the performance of evolutionary algorithms on continuous multiobjective optimization problems. SBS divides the search space into many small segments according to the evolutionary information feedback from the set of current optimal solutions. Two operations, micro-jumping and macro-jumping, are implemented upon these segments in order to guide an efficient information exchange among “good” individuals. Moreover, the running of SBS is adaptive according to the current evolutionary status. SBS is activated only when the population evolves slowly, depending on general genetic operators (e.g., mutation and crossover). A comprehensive set of 36 test problems is employed for experimental verification. The influence of two algorithm settings (i.e., the dimensionality and boundary relaxation strategy) and two probability parameters in SBS (i.e., the SBS rate and micro-jumping proportion) are investigated in detail. Moreover, an empirical comparative study with three representative variation operators is carried out. Experimental results show that the incorporation of SBS into the optimization process can improve the performance of evolutionary algorithms for multiobjective optimization problems.en
dc.publisherIEEE Pressen
dc.subjectHybrid evolutionary algorithmsen
dc.subjectmultiobjective optimizationen
dc.subjectsegment-based searchen
dc.subjectvariation operatorsen
dc.titleEvolutionary algorithms with segment-based search for multiobjective optimization problemsen
dc.researchgroupCentre for Computational Intelligenceen
dc.funderThis work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1, in part by the National Natural Science Foundation of China (Major International Joint Research Project) under Grant 71110107026, in part by EU FP7-Health under Grant 242193, in part by the EPSRC Industrial Case under Grant 11220252, and in part by the Education Department and “Qinglan Engineering” of Jiangsu Province, China.en
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record