An Adaptive Dynamic Parameter Multi-objective Optimization Algorithm

In order to improve the convergence speed of the multi-objective optimization algorithm while obtaining good distribution and diversity, an adaptive dynamic parameter multi-objective optimization algorithm is proposed (ADPMO). The new algorithm consists of three main strategies. Firstly, a new mutation method based on individual competition mechanism integrated with k-means clustering is proposed, which updates the velocity and position information of the individuals that have failed to compete in each cluster, for improve the diversity of the solution set and avoid premature convergence. Secondly, an adaptively dynamical parameters strategy is proposed. In the process of speed updating, parameters that change dynamically with the number of population iterations, for enhance the convergence speed and convergence of the algorithm. At last, a cross-mutation strategy is introduced, for making the population out of collapse state and increase the diversity of the optimal solution set. Compared with other state-and-art multi-objective algorithms on the two types of benchmark functions, it is verified that the individuals can more converge faster to the real Pareto frontier with good distribution.