Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A multi-objective evolution algorithm (MOEA) is presented to automatically determine the parameters in Op-Amp synthesis where the cost functions (e.g., minimizing the por dissipation and the chip area) and the constraint functions (e.g., the user-defined specifications) can be modeled as polynomials of the design variables. The proposed algorithm is based on MOEA which does not use ighting coefficients in converting multiple objectives into single objective. A constraint handling strategy without penalty parameters is proposed to avoid the difficulty of penalty parameter selection. Moreover, an elitist maintaining scheme is utilized to keep the evenness of the Pareto front. Simulations over several benchmark functions validate the efficiency of the proposed algorithm for the evenness of population distribution and the convergence to the Pareto front. Numerical experiments of a Miller compensated two-stage Op-Amp show that the proposed MOEA is able to achieve better performance than NSGA-II+PCH, GA+SPF and GA+PCH.
['Jili Tao', 'Qinru Fan', 'Xiaoming Chen', 'Yong Zhu']
I am writing a research paper on a idea Constraint multi-objective automated synthesis for CMOS operational amplifier
[{"from": "human", "value": "I am writing a research paper on a idea Constraint multi-objective automated synthesis for CMOS operational amplifier"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A multi-objective evolution algorithm (MOEA) is presented to automatically determine the parameters in Op-Amp synthesis where the cost functions (e.g., minimizing the por dissipation and the chip area) and the constraint functions (e.g., the user-defined specifications) can be modeled as polynomials of the design variables. The proposed algorithm is based on MOEA which does not use ighting coefficients in converting multiple objectives into single objective. A constraint handling strategy without penalty parameters is proposed to avoid the difficulty of penalty parameter selection. Moreover, an elitist maintaining scheme is utilized to keep the evenness of the Pareto front. Simulations over several benchmark functions validate the efficiency of the proposed algorithm for the evenness of population distribution and the convergence to the Pareto front. Numerical experiments of a Miller compensated two-stage Op-Amp show that the proposed MOEA is able to achieve better performance than NSGA-II+PCH, GA+SPF and GA+PCH."}]