A Hybrid Evolutionary Algorithm for Multi-objective Optimization Problem
Zan Dong-ping · Yunchou yu guanli · 2012
A hybrid algorithm combining quantum computing and NSGA-II is designed for multi-objective optimization problem.It makes use of the advantages of quantum algorithm and NSGA-II to balance between exploitation and exploration.In hybrid algorithm,Qubit is used to encode solutions to the problem into individuals.The population is updated based on operators of Quantum rotation gate,Scattered crossover and Gaussian mutation.When addressing exploitation,a solution's distance to an ideal point in objective space is used to evaluate the solution.While in exploration a solution is evaluated by use of classifications of Pareto fronts and the crowding distance between individuals in NSGA-II.Finally the hybrid algorithm is tested on a classic benchmark problem ZDTS.By comparing and analyzing several performance metrics for Pareto solution sets,it is demonstrated that the hybrid algorithm is superior to widely used NSGA-II in both proximity to optimal Pareto front and the uniform distribution of solutions.