Document Type
Thesis
Date of Award
Spring 5-31-2017
Degree Name
Master of Science in Computer Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
MengChu Zhou
Second Advisor
James M. Calvin
Third Advisor
Qing Gary Liu
Abstract
A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE.
Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging.
Recommended Citation
Chatterjee, Ishani, "Multi-population-based differential evolution algorithm for optimization problems" (2017). Theses. 17.
https://digitalcommons.njit.edu/theses/17
