Computational Intelligence and Neuroscience / 2019 / Article / Fig 6

Research Article

An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism

Figure 6

(a) Average performance score obtained by eleven algorithms over all test problems of different numbers of objectives in terms of the HV and (b) average performance score obtained by ten algorithms on dimensions for different test problems in terms of the HV, Dx for DTLZ, and Wx for WFG. The values of the proposed OBEA are connected by a solid red line.