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The Parallel Genetic Algorithm-Based Multiobjective Optimization Technique for Analog Circuit Optimizer

By Kasin Prakobwaitayakit and Nobuo Fujii

Abstract

Abstract: The evolutionary multiobjective optimization technique for analog circuit optimizer is presented in this paper. the technique uses a Parallel Genetic Algorithm(PGA) to identifies multiple “good ” solutions from a multiobjective fitness landscape which are tuned using a local hill-climbing algorithm. The PGA is used to provide a nature niching mechanism that has considerable computational advantages and generate as many “good ” design solutions as possible. The local hill-climbing algorithm restricts the search in the basin of attraction of a design solution, thus tries to tune the design up-to the sub-optimum. The main advantages of this approach are 1) realizing a non-fixed-topology optimization by combining PGA and local hill-climbing with circuit simulator, and 2) capability to find multiple “good ” optimization points simultaneously using less time consumption. Some electronic circuit design examples are shown

Topics: Key-words, Circuit optimization, Analog circuit design, Multi-objective optimization
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.412.9874
Provided by: CiteSeerX
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