Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
A genetic algorithm high-level optimizer for complex datapath and data-flow digital systems
Applied Soft Computing
Genetic learning based fault tolerant models for digital systems
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
This paper presents a genetic based decoupled optimal design method for power electronics circuit design using an adaptive collaboration approach in a cooperative coevolutionary environment. The circuit parameters of the power conversion stage and the feedback network of a buck regulator are optimized through two parallel coadaptive genetic based optimization processes. The best candidate of the tunable parameters in one evolutionary process for the design of the power conversion stage is merged to the other evolutionary process for the design of the feedback network as untunable factors through a collaboration controller in which the collaboration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value of the genes representing the circuit design parameters in each generation. The proposed design methodology is suitable for parallel computation resulting in considerable improvement in searching efficiency. Simulated results of the design of a buck regulator with the proposed approach were verified with experimental results from the actual hardware implementation. It showed that the design with the proposed scheme was compatible with the design specification.