Evaluation of Dissolved Organic Matter Removals through WWT and SAT Using Pilot-Scale and Lab-Scale Reactors

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Takabe Y., Kameda I., Suzuki R., Nishimura F., Kusuda Y., Phattarapattamawong S., Itoh S.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2019

Volume number: 230

Issue number: 6

ISBN: 9781728133614

นอก: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078827216&doi=10.1109%2fECTI-CON47248.2019.8955176&partnerID=40&md5=d107d9159d311c47f16e111fcc3a3241

ภาษา: English-Great Britain (EN-GB)

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บทคัดย่อ

The Artificial Bee Colony algorithm is originally designed for solving numerical optimization problems, whereas the Travelling Salesman Problem is classified as a combinatorial optimization one. This paper proposes a combinatorial variant of the Artificial Bee Colony algorithm by using reinforcement learning update. Reinforcement values are introduced, and positive reinforcement is given to the selected cities when a better solution is found by an employed bee. On the contrary, negative reinforcement is assigned to the selected cities when a worse solution is found. Onlooker bees then select cities to update their solutions according to these reinforcement values. The performance of the proposed algorithm is tested on six benchmark problems. The results show that the algorithm with the reinforcement learning update provides better solutions than the algorithm without the reinforcement learning update in five out of six benchmark problems. The convergence rates of the algorithm with the reinforcement learning update are also faster than those of the algorithm without the reinforcement learning update. ฉ 2019 IEEE.

คำสำคัญ

artificial bee colony, Reinforcement learning, Travelling salesman problem