Optimal FSM's State Encoding for Low power using Dynamic Boundary Difference Mutation Strategy in Evolutionary Programming

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 1

Year of Publication : 2023

Authors : Deepti Raj, AB Kalpana, Manoj Kumar Singh

10.14445/23488379/IJEEE-V10I1P115

How to Cite?

Deepti Raj, AB Kalpana, Manoj Kumar Singh, "Optimal FSM's State Encoding for Low power using Dynamic Boundary Difference Mutation Strategy in Evolutionary Programming," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 1, pp. 160-167, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I1P115

Abstract:

In this paper, a new computation intelligent approach based on evolutionary programming is applied to minimize the weighted hamming distance among states to reduce the switching frequency for low-power design in Finite State Machines. A mutation strategy is proposed to carry better exploration of solution space by deploying a dynamic differential approach between the current solution position and solution domain boundary limits. The proposed method is compared against the performances of the standard form of mutation strategies based on the self-adaptive version of Gaussian and Cauchy mutation and an advanced version of particle swarm optimization. The different combinations of Gaussian and Cauchy mutations are also examined. The performances of the proposed solution were superior and computationally efficient in comparision to all others. The robustness against variability is excellent over a large number of runs.

Keywords:

Finite state machine, State encoding, Low power, Gaussian mutation, Cauchy mutation, Evolutionary programming.

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