An improvement decomposition-based multi-objective evolutionary algorithm with uniform design

How to quickly find a set of solutions with good diversity and convergence is the main goal of multi-objective optimization evolutionary algorithms (MOEAs). In this paper, a crossover operator based on uniform design and selection strategy based on decomposition is designed to help MOEAs to improve the search efficiency, and an improvement decomposition-based multi-objective evolutionary algorithm with uniform design is proposed. Firstly, a multi-objective problem is transformed into a set of single problems based on a set of direction vectors, and all single problems are optimized simultaneously. Secondly, a crossover operator based on uniform design which can search decision space along the descent (ascent) directions is designed to improve the search efficiency of the algorithm. Thirdly, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Moreover, a selection strategy is designed to help the crossover operators to balance between the global searching and the local searching. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.