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

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. Key-words: Circuit optimization, Analog circuit design, Multi-objective optimization, Parallel genetic algorithm, Local hill-climbing technique, CAD tools.