A new simple and highly efficient multi-objective optimal evolutionary algorithm

Chuan Shi, Yan Li, Lishan Kang

Abstract: Multi-objective optimal evolutionary algorithms (MOEA) are effective algorithms to solve multi-objective optimal problem (MOP). Because ranking which used by most MOEAs has some disadvantages, We propose a new method that uses better function to compare candidate solutions and tree structure to express the relationship of solutions. Experiments show that the new algorithm can converge to the Pareto front, and maintains the diversity of population. When the algorithm is extended to a MOP with constraints, it can also get a good result. Most important of all, the algorithm is simple but highly efficient.