运筹与管理2012，Vol.21Issue(4)：15-21,7.
面向多目标优化的一种混合进化算法
A Hybrid Evolutionary Algorithm for Multi-objective Optimization Problem
摘要
Abstract
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.关键词
运筹学/算法改进/量子计算/非支配排序遗传算法/有效解集Key words
operations research/ improvement of algorithm/ quantum computing/ NSGA-II/ Pareto solution set分类
信息技术与安全科学引用本文复制引用
刘锋,王建军,杨德礼,昝冬平..面向多目标优化的一种混合进化算法[J].运筹与管理,2012,21(4):15-21,7.基金项目
国家自然科学基金资助项目(70902033,71271039) （70902033,71271039）
辽宁省博士启动基金资助项目(20081093) （20081093）
中央高校基本科研业务费专项基金资助项目(DUT11SX10) （DUT11SX10）
