Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker.  design a genetic algorithm for this purpose.  compose multiple fitness functions to guide the search, where each fitness function is equal to a ighted sum of the normalized objective functions and  apply an experimental design method called uniform design to select the ights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions,  design a selection scheme to maintain a good and diverse population. In addition,  apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier.
['Yiu-Wing Leung', 'Yuping Wang']
I am writing a research paper on a idea Multiobjective programming using uniform design and genetic algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Multiobjective programming using uniform design and genetic algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker.  design a genetic algorithm for this purpose.  compose multiple fitness functions to guide the search, where each fitness function is equal to a ighted sum of the normalized objective functions and  apply an experimental design method called uniform design to select the ights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions,  design a selection scheme to maintain a good and diverse population. In addition,  apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier."}]