JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Sinusoidal Map Jumping Gravity Search Algorithm Based on Asynchronous Learning |
| 영문제목(English Title) |
Sinusoidal Map Jumping Gravity Search Algorithm Based on Asynchronous Learning |
| 저자(Author) |
Xinxin Zhou
Guangwei Zhu
|
| 원문수록처(Citation) |
VOL 18 NO. 03 PP. 0332 ~ 0343 (2022. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm’s shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance. |
| 키워드(Keyword) |
Asynchronous Learning
Gravitational Search Algorithm
Levy Flight
Sinusoidal Map
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| 파일첨부 |
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