TY - GEN
T1 - Differential evolution with two subpopulations
AU - Lynn, Nandar
AU - Mallipeddi, Rammohan
AU - Suganthan, Ponnuthurai Nagaratnam
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In this paper, differential evolution with two subpopulations is proposed for balancing exploration and exploitation capabilities. The first population is responsible for exploring over the search space to find good regions using only its own subpopulation. The second subpopulation is responsible for exploiting good regions. The exploitation-oriented sub-population is permitted to make use of the whole population to select best solution candidates to generate offspring. Hence, this heterogeneous one-way information transfer allows the exploration subpopulation to maintain diversity even when exploitation group converges. This is an efficient realization of population based algorithm enabling simultaneous use of highly exploitative and explorative characteristics simultaneously. Hence, this approach can be an effective substitute for memetic algorithms in the real-parameter optimization domain. The performance of the algorithm is evaluated using the shifted and rotated benchmark problems. To verify the performance of the proposed algorithm, it is also applied to solve the unit commitment problem by considering 10 and 20 unit power systems over 24 h scheduling period.
AB - In this paper, differential evolution with two subpopulations is proposed for balancing exploration and exploitation capabilities. The first population is responsible for exploring over the search space to find good regions using only its own subpopulation. The second subpopulation is responsible for exploiting good regions. The exploitation-oriented sub-population is permitted to make use of the whole population to select best solution candidates to generate offspring. Hence, this heterogeneous one-way information transfer allows the exploration subpopulation to maintain diversity even when exploitation group converges. This is an efficient realization of population based algorithm enabling simultaneous use of highly exploitative and explorative characteristics simultaneously. Hence, this approach can be an effective substitute for memetic algorithms in the real-parameter optimization domain. The performance of the algorithm is evaluated using the shifted and rotated benchmark problems. To verify the performance of the proposed algorithm, it is also applied to solve the unit commitment problem by considering 10 and 20 unit power systems over 24 h scheduling period.
KW - Differential evolution
KW - Exploitation
KW - Exploration
KW - Memetic algorithms
KW - Power systems
KW - Scheduling
KW - Subpopulations
KW - Unit commitment problem
UR - https://www.scopus.com/pages/publications/84946110906
U2 - 10.1007/978-3-319-20294-5_1
DO - 10.1007/978-3-319-20294-5_1
M3 - Conference contribution
AN - SCOPUS:84946110906
SN - 9783319202938
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 13
BT - Swarm, Evolutionary, and Memetic Computing - 5th International Conference, SEMCCO 2014, Revised Selected Papers
A2 - Suganthan, Ponnuthurai Nagaratnam
A2 - Panigrahi, Bijaya Ketan
A2 - Das, Swagatam
PB - Springer Verlag
T2 - 5th International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2014
Y2 - 18 December 2014 through 20 December 2014
ER -