ข้อมูลนักวิจัย : กาญจนา ดาวเด่น
รายละเอียดงานวิจัย
ชื่องานวิจัย : Using Non-straight Line Updates in Shuffled Frog Leaping Algorithm
ปีที่จัดทำ : 2563
ผู้จัดทำ : กาญจนา ดาวเด่น (
เจ้าของงานวิจัย
)
ประเภทงานวิจัย :
การประชุมวิชาการระดับนานาชาติ
สถานะ :
งานวิจัยได้รับการตีพิมพ์
แหล่งทุน :
แหล่งตีพิมพ์เผยแพร่ : Intelligent Computing and Optimization Proceedings of the 3rd International Conference on Intelligent Computing and Optimization 2020 (ICO 2020) ปี (2020)
วันที่ตีพิมพ์ : 22-23 April
หน้าที่ : 954-963
ค่าถ่วงน้ำหนัก : 0.4
งบประมาณ : 0.00
บทคัดย่อ :
This paper presents an improved version of the shuffled frog leaping algorithm (SFLA), called the drunken SFLA. The updating process in the original SFLA moves a solution toward a hopefully better solution in a straight-line manner while the algorithm is trying to search for the optimal solution, however, for some types of problems, moving in a straight-line manner might not converge effectively or might not converge at all. Since the optimal solution might not be on the straight line between solutions while the algorithm is progressing. The idea is to add the angle to the updating process in order to expand the search space in each step. The updating process of ‘Drunken SFLA' is no longer in a straight line. This paper explores the possibility of modifying the updating process of SFLA in a Non-Straight Line manner. The convergence performance results of the proposed drunken SFLA and the original SFLA are presented.
Keywords: Optimization · Meta-heuristic · Shuffled frog leaping algorithm
(SFLA) • Truncated Gaussian distribution
This paper presents an improved version of the shuffled frog leaping algorithm (SFLA), called the drunken SFLA. The updating process in the original SFLA moves a solution toward a hopefully better solution in a straight-line manner while the algorithm is trying to search for the optimal solution, however, for some types of problems, moving in a straight-line manner might not converge effectively or might not converge at all. Since the optimal solution might not be on the straight line between solutions while the algorithm is progressing. The idea is to add the angle to the updating process in order to expand the search space in each step. The updating process of ‘Drunken SFLA' is no longer in a straight line. This paper explores the possibility of modifying the updating process of SFLA in a Non-Straight Line manner. The convergence performance results of the proposed drunken SFLA and the original SFLA are presented.
Keywords: Optimization · Meta-heuristic · Shuffled frog leaping algorithm
(SFLA) • Truncated Gaussian distribution
หมายเหตุ :
Daoden K., Thaiupathump T. (2020) Using Non-straight Line Updates in Shuffled Frog Leaping Algorithm. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_80
Daoden K., Thaiupathump T. (2020) Using Non-straight Line Updates in Shuffled Frog Leaping Algorithm. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_80