Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a multiobjective evolutionary algorithm based on MOEA/D [1] for solving multiobjective optimization problems. MOEA/D decomposes a multiobjective optimization problem into many single-objective subproblems. The objective of each subproblem is a ighted aggregation of the original objectives. Using evenly distributed ight vectors on subproblems, solutions to subproblems form a set of ll-spread approximated Pareto optimal solutions to the original problem. In MOEA/D, each individual in the population represents the current best solution to one subproblem. Mating selection is carried out in a uniform and static manner. Each individual/subproblem is selected/solved once at each generation, and the mating pool of each individual is determined and fixed based on the distance beten ight vectors on the objective space.  propose an adaptive mating selection mechanism for MOEA/D. It classifies subproblems into solved ones and unsolved ones and selects only individuals of unsolved subproblems. Besides, it dynamically adjusts the mating pools of individuals according to their distance on the decision space. The proposed algorithm, MOEA/D-AMS, is compared with two versions of MOEA/D using nine continuous functions. The experimental results confirm the benefits of the adaptive mating selection mechanism.
['Tsung Che Chiang', 'Yung Pin Lai']
I am writing a research paper on a idea MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism
[{"from": "human", "value": "I am writing a research paper on a idea MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a multiobjective evolutionary algorithm based on MOEA/D [1] for solving multiobjective optimization problems. MOEA/D decomposes a multiobjective optimization problem into many single-objective subproblems. The objective of each subproblem is a ighted aggregation of the original objectives. Using evenly distributed ight vectors on subproblems, solutions to subproblems form a set of ll-spread approximated Pareto optimal solutions to the original problem. In MOEA/D, each individual in the population represents the current best solution to one subproblem. Mating selection is carried out in a uniform and static manner. Each individual/subproblem is selected/solved once at each generation, and the mating pool of each individual is determined and fixed based on the distance beten ight vectors on the objective space.  propose an adaptive mating selection mechanism for MOEA/D. It classifies subproblems into solved ones and unsolved ones and selects only individuals of unsolved subproblems. Besides, it dynamically adjusts the mating pools of individuals according to their distance on the decision space. The proposed algorithm, MOEA/D-AMS, is compared with two versions of MOEA/D using nine continuous functions. The experimental results confirm the benefits of the adaptive mating selection mechanism."}]