Using Good Nodes Set Principle to Evolution Strategy for Constrained Optimization

Incorporating orthogonal design to enhance the crossover operator of the evolution strategy (ES) can make the resulting evolutionary algorithm more robust and statically sound. But its precision is restricted by dimension of search space. Good nodes set (GNS) is a concept in number theory. This paper presents a new evolution strategy that effectively combines GNS principle with crossover operator to handle constrained optimization problems (COPs). The proposed method has achieved the same sound results as the orthogonal method does, but not to be restricted by the dimension of the space. The simplex selected and diversity mechanism is used to enrich the exploration and exploitation abilities of the approach proposed. Experiment results on a set of benchmark problems show the efficiency of the algorithm.