TY - GEN
T1 - Enhancing Differential Evolution Algorithm
T2 - 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
AU - Salgotra, Rohit
AU - Mirjalili, Seyedali
AU - Gandomi, Amir H.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Differential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealing-based scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate (CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.
AB - Differential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealing-based scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate (CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.
KW - CEC 2017
KW - CEC 2021
KW - Differential evolution
KW - LSHADE
KW - numerical optimization
UR - http://www.scopus.com/inward/record.url?scp=85151742303&partnerID=8YFLogxK
U2 - 10.1109/ISCMI56532.2022.10068469
DO - 10.1109/ISCMI56532.2022.10068469
M3 - Conference contribution
AN - SCOPUS:85151742303
T3 - 2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
SP - 235
EP - 240
BT - 2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 November 2022 through 27 November 2022
ER -