PASSIOT: A pareto-optimal multi-objective optimization approach for synthesis of analog circuits using sobol' indices-based engine

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies.The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N 2 ).

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PASSIOT: A pareto-optimal multi-objective optimization approach for synthesis of analog circuits using sobol' indices-based engine

Cited by 2 publications

References 9 publications

Review on VLSI design using optimization and self-adaptive particle swarm optimization

Review on VLSI design using optimization and self-adaptive particle swarm optimization

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

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