Non-even spread NSGA-II and its application to conflicting multi-objective compatible control

Authors:
Qingsong Hu;Lihong Xu;Erik D. Goodman
Affiliations:
Tongji University, Shanghai, China;Tongji University, Shanghai, China;Michigan State University, East Lansing, MI, USA
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a sound method to deal with the multi-objective optimization problem, and even spread Pareto front preserving strategy is one of its two key principles. However, especially for some dynamic problems, the most interested area is certain special area among the Pareto front. To meet this requirement, the non-even Pareto front spread preserving principle is proposed and is taken as the optimization tool for the multi-objective compatible control problem (MOCCP). To decrease the real-time computation load at every control step, based on the tight relation between the system states of the neighboring sampling instants, an iterative control algorithm is presented. The stability preference selection strategy in the algorithm tends to produce a stable controller in face of the Pareto front with the divergent or oscillating segment. To further decrease the computation time, adaptable population corresponding with the control process is adopted. Comparative simulation example illustrates the validity.