Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved ...Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.展开更多
基于两个体比较的交互式遗传算法(Interactive Genetic Algorithm based on paired comparison,PC-IGA)允许用户在每次评估过程中比较两个个体并从中选择一个优胜者,以代替传统的用户评分方式,从而减轻用户的精神压力.但是,PC-IGA中用...基于两个体比较的交互式遗传算法(Interactive Genetic Algorithm based on paired comparison,PC-IGA)允许用户在每次评估过程中比较两个个体并从中选择一个优胜者,以代替传统的用户评分方式,从而减轻用户的精神压力.但是,PC-IGA中用户比较次数太多,加重了用户的生理疲劳.为此,本文提出一种新的用户评估方式——锦标赛选择,并给出锦标赛选择交互式遗传算法(Interactive Genetic Algorithm Based on Tournament Selection,TS-IGA)的关键技术和实现步骤.将该算法应用于服装色彩优化系统,研究了种群规模和子种群规模的选择对算法性能的影响.最后,将该算法与PC-IGA进行对比实验,结果表明本文提出的算法在选择合适的子种群规模的情况下,能有效减少用户的比较次数和算法收敛时间,从而减轻用户疲劳.展开更多
文摘Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.
文摘基于两个体比较的交互式遗传算法(Interactive Genetic Algorithm based on paired comparison,PC-IGA)允许用户在每次评估过程中比较两个个体并从中选择一个优胜者,以代替传统的用户评分方式,从而减轻用户的精神压力.但是,PC-IGA中用户比较次数太多,加重了用户的生理疲劳.为此,本文提出一种新的用户评估方式——锦标赛选择,并给出锦标赛选择交互式遗传算法(Interactive Genetic Algorithm Based on Tournament Selection,TS-IGA)的关键技术和实现步骤.将该算法应用于服装色彩优化系统,研究了种群规模和子种群规模的选择对算法性能的影响.最后,将该算法与PC-IGA进行对比实验,结果表明本文提出的算法在选择合适的子种群规模的情况下,能有效减少用户的比较次数和算法收敛时间,从而减轻用户疲劳.
