Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for ight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the ight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front ll in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for ight vectors, the whole Pareto-front can be ll approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches.
['Shaofu Yang', 'Jun Wang', 'Qingshan Liu']
I am writing a research paper on a idea Multiple-objective optimization based on a two-time-scale neurodynamic system
[{"from": "human", "value": "I am writing a research paper on a idea Multiple-objective optimization based on a two-time-scale neurodynamic system"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for ight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the ight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front ll in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for ight vectors, the whole Pareto-front can be ll approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches."}]