TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Knee-driven many-objective sine-cosine algorithm |
| 영문제목(English Title) |
Knee-driven many-objective sine-cosine algorithm |
| 저자(Author) |
Hongxia Zhao
Yongjie Wang
Maolin Li
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0335 ~ 0352 (2023. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many-objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems. |
| 키워드(Keyword) |
Evolutionary computations
Many-objective optimization
Knee points
Sine cosine algorithm
Latin square sampling
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| 파일첨부 |
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