A Fast-Stable Optimization Algorithm for Multi-objective Population Migration

This paper presents a fast-stable population migration algorithm for multi-objective optimization to solve multi-objective optimization problems. Based on the concept of Pareto non-domination and guided by a global optimization experiments, this algorithm adopts dynamic mutation operator and the entire population migrating method to increase the algorithm convergence speed and population diversity. Finally it is tested for algorithm performance by using nine standard multi-objective functions, which is compared with simulation of algorithms such as SPEA, NSGAII and so on, The test shows that proposed algorithm is much better in the way of convergence, diversity and solution distribution.