TY - GEN
T1 - Hybrid Political Algorithm Approach for Engineering Optimization Problems
AU - Su, Yu Ting
AU - Liu, En Hauh
AU - Li, Tzuu Hseng S.
N1 - Funding Information:
The support of this work in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2221-E-006-196-MY3 is gratefully acknowledged.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Political Optimizer is a metaheuristic algorithm proposed by scholars recently. It reflects good convergence speed as well as exploitation and exploration capabilities in problems relevant to finding optimal solutions. However, regarding the global optimum, it still has spaces to be improved. This study proposed a Hybrid Political Algorithm, which makes balanced modifications to the exploration process and algorithm functions to effectively improve searching for the optimal solutions in the mathematic problems of Congress on Evolutionary Computation and engineering problems by enabling the particles to move smarter in the computation process. Based on the experimental results, improving the performance of algorithm functions using the methods proposed in this study can also help avoid falling into the local optimum. In addition, compared to other algorithms, this improved algorithm can generate more accurate results.
AB - Political Optimizer is a metaheuristic algorithm proposed by scholars recently. It reflects good convergence speed as well as exploitation and exploration capabilities in problems relevant to finding optimal solutions. However, regarding the global optimum, it still has spaces to be improved. This study proposed a Hybrid Political Algorithm, which makes balanced modifications to the exploration process and algorithm functions to effectively improve searching for the optimal solutions in the mathematic problems of Congress on Evolutionary Computation and engineering problems by enabling the particles to move smarter in the computation process. Based on the experimental results, improving the performance of algorithm functions using the methods proposed in this study can also help avoid falling into the local optimum. In addition, compared to other algorithms, this improved algorithm can generate more accurate results.
UR - http://www.scopus.com/inward/record.url?scp=85143380969&partnerID=8YFLogxK
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U2 - 10.1109/ICSSE55923.2022.9948232
DO - 10.1109/ICSSE55923.2022.9948232
M3 - Conference contribution
AN - SCOPUS:85143380969
T3 - ICSSE 2022 - 2022 International Conference on System Science and Engineering
SP - 104
EP - 109
BT - ICSSE 2022 - 2022 International Conference on System Science and Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on System Science and Engineering, ICSSE 2022
Y2 - 26 May 2022 through 29 May 2022
ER -