Objective-reduction using the least squares method
基于最小二乘法的高维目标减少算法

Multi-objective evolutionary algorithms are widely applied to many real world problems; however, most of the papers merely focus on the problems with two or three objectives, in which objective-reduction has become a research focus for many multi-objective optimization. From the views of decision makers, this paper proposes a new objective-reduction using the least squares method(ORLSM). This algorithm fits each objective into multi-straight lines and determines the most redundant objective couples between each two slope vectors for searching the most redundant objective. Moreover, in view of the variety of individual dominance relation after the number of objectives is decreased, a performance assessment metric based on the changed Pareto dominance ratio(CDR) is also proposed. From an extensive comparative study with two similar algorithms in terms of inverted generational distance(IGD), CDR, and running time on 3 test problems, ORLSM indicates its superiority in overall performances; CDR and IGD almost have the evaluation in assessments.