Multi objective optimization of a LNA using genetic algorithm based on NSGA-II

Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

“…The algorithm was written by MATLAB and the circuit simulated by Hspice RF. In [25], NSGA-II and Hspice RF simulation were combined to produce an accurate tool for LNA optimization. Ref.…”

“…Heuristic search algorithm Simulator [15] GA, SA, LM 2 ADS [16] IGA a ADS [17] Layered encoding structure using combination of GA and PSO ADS [18] GA ADS [19] MOPSO-CD b and NSGA-II c ADS [20] PSO _ [21] Combination of GA and multi objective PSO (programmed in C++ software) ADS [14] GA, LM Hspice [22] Modified genetic algorithm and PSO Hspice RF [23] Genetic algorithm and Multi-Objective PSO Hspice RF [24] Multi objective genetic algorithm Hspice RF [25] NSGA-II Hspice RF [26] Pareto-based multi-objective genetic algorithm Hspice RF a Interactive genetic algorithm. b Multi-Objective Optimization algorithm PSO incorporating the mechanism of the crowding distance technique.…”

“…is a combination of a heuristic search algorithm implemented in MATLAB and Hspice/Hspice RF as circuit simulator which can be linked automatically [22][23][24][25][26]. In Table 1, the reported researches in this field are summarized.…”

Intelligent GA optimizer for single supply operational amplifier and MMIC low noise amplifier

“…These fitness measurements are then further evaluated using the normalized weighted-sum approach to identify the overall fitness of a candidate solution. Equation (4)(5)(6)(7)(8) show the fitness function evaluation for power gain (dB(S(2,1)), noise figure (NFmin_out), drain current (ID), as well as circuit stability factors (mu_load & mu_source). A penalty value,  is given to the fitness function when the the required constraints are not complied.…”

“…In this research, Genetic Algorithm (GA) that allows computers to solve various difficult problems with analogical genetic chromosome representations and fitness survival is used to optimize the design performance of Single-Supply Op-Amp and MMIC LNA. In term of computational intelligence, GA has been widely recognized for its robustness to deal with various optimization problems [8][9]. The research is firstly organized to investigate the capability of GA in solving single objective Single-Supply Op-Amp.…”