Sizing mixed-mode circuits by multi-objective evolutionary algorithms

This paper presents a systematic methodology for the generation of high-level performance models for analog component blocks. The transistor sizes of the circuit-level implementations of the component blocks along with a set of geometry constraints applied over them define the sample space. A Halton sequence generator is used as a sampling algorithm. Performance data are generated by simulating each sampled circuit configuration through SPICE. Least squares support vector machine (LS-SVM) is used as a regression function. Optimal values of the model hyper parameters are determined through a grid search-based technique and a genetic algorithm- (GA-) based technique. The high-level models of the individual component blocks are combined analytically to construct the high-level model of a complete system. The constructed performance models have been used to implement a GA-based high-level topology sizing process. The advantages of the present methodology are that the constructed models are accurate with respect to real circuit-level simulation results, fast to evaluate, and have a good generalization ability. In addition, the model construction time is low and the construction process does not require any detailed knowledge of circuit design. The entire methodology has been demonstrated with a set of numerical results.

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“…It may be noted that multiobjective optimization algorithms [40] can also be used in the proposed approach for solving (25).…”

Section: Discussionmentioning

confidence: 99%

A Methodology for Generation of Performance Models for the Sizing of Analog High-Level Topologies

2011

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“…MOEA/D-DE [101] is also applied on these problems as well and for it an optimized spacing between the elements of linear array while achieving the best possible trade-off between the above mentioned two design objective functions [152]. Furthermore, MOEA/D-DE [101] is applied on problems formulated in [57] and have found better optimally sized two mixed-mode circuits including positive second generation current conveyor and current feedback operational amplifier as compared to NSGA-II [42].…”

Section: Decomposition Based Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Khan¹,

Salhi²,

Jan³

et al. 2016

ijacsa

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Abstract-In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of representative Pareto optimal solutions in a single run. Classical MOEAs are broadly in three main groups: the Pareto dominance based MOEAs, the Indicator based MOEAs and the decomposition based MOEAs. Those based on decomposition and indicator functions have shown high search abilities as compared to the Pareto dominance based ones. That is possibly due to their firm theoretical background. This paper presents state-of-the-art MOEAs that employ decomposition and indicator functions as fitness evaluation techniques along with other efficient techniques including those which use preference based information, local search optimizers, multiple ensemble search operators together with self-adaptive strategies, metaheuristics, mating restriction approaches, statistical sampling techniques, integration of Fuzzy dominance concepts and many other advanced techniques for dealing with diverse optimization and search problems

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“…It should be mentioned that including the parameter of a genotype length in the ranking operator represents the case of multiobjective evolution, where to each objective corresponds its own weight, what in its turn could be evolved [26]. However, in our case, we use the second objective as an ad hoc parameter, whose purpose is to handle the evolution and chromosome behavior in the right way.…”

Section: ) Rankingmentioning

confidence: 99%

Synthesis of Time-to-Amplitude Converter by Mean CoeVolution with Adaptive Parameters

Sapargaliyev¹,

Kalganova

2011

JSEA

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Sizing mixed-mode circuits by multi-objective evolutionary algorithms

Cited by 4 publications

References 15 publications

A Methodology for Generation of Performance Models for the Sizing of Analog High-Level Topologies

A Methodology for Generation of Performance Models for the Sizing of Analog High-Level Topologies

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Synthesis of Time-to-Amplitude Converter by Mean CoeVolution with Adaptive Parameters

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