Multi-spatial information joint guidance evolutionary algorithm for dynamic multi-objective optimization with a changing number of objectives - Ma, Xuemin; Sun, Hao; Hu, Ziyu; Wei, Lixin; Yang, Jingming (Neural computing & applications, 2023)
Existing research on dynamic multi-objective optimization problems involving changes in the number of objectives has received little attention, but it is widespread in practical applications. This problem would cause the expansion or contraction of the manifold in the objective space. If it is accompanied by changes in Pareto set/front (PS/PF), the problem becomes more complex. However, several dynamic response techniques have been developed to handling this kind of dynamics. Faced with these issues, a multi-spatial information joint guidance evolutionary algorithm is proposed. To more accurately identify the optimal solutions after the change, a space adaptive transfer strategy is introduced, which adopts the geodesic flow kernel method to extract spatial information at different times. Afterwards it adaptively transfers the space via different changes to generate new individuals. In order to improve the diversity after the change, a dual space multi-dimensional joint sampling strategy is proposed. It fully combines the individual information in the objective and the decision space. Then the promising solutions are sampled in multiple dimensions near the representative individuals. Comprehensive experiments are conducted on 15 benchmark functions with a varying number of objectives and PS/PF. Simulation results verify the capability of the proposed algorithm.