Chaotic GEP algorithm for dynamic multi-objective optimization

Dynamic Multi-objective Optimization (DMO) is a new research topic in the field of evolutionary computation in recent years. As Gene Expression Programming (GEP) has a powerful search capability, a new algorithm for DMO called D-GEP Chaotic NSGA-II is proposed. The algorithm is designed on the classic multi-objective optimization algorithm NSGA-II to make it suitable for DMO, while using GEP for encoding and chaotic variables for generating initial population. The experiments on test problems of three different types have shown that the algorithm has better performance on convergence, diversity and the breadth of the distribution.