TY - GEN
T1 - Metaheuristic Optimization on Tensor-Type Solution via Swarm Intelligence and Its Application in the Profit Optimization in Designing Selling Scheme
AU - Phoa, Frederick Kin Hing
AU - Liu, Hsin Ping
AU - Chen-Burger, Yun Heh (Jessica)
AU - Lin, Shau Ping
N1 - Funding Information:
This work is partially supported by the Academia Sinica grant number AS-TP-109-M07 and the Ministry of Science and Technology (Taiwan) grant numbers 107-2118-M-001-011-MY3 and 109-2321-B-001-013.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/7/7
Y1 - 2021/7/7
N2 - Nature-inspired metaheuristic optimization has been widely used in many problems in industry and scientific investigations, but their applications in designing selling scheme are rare because the solution space in this kind of problems is usually high-dimensional, and their constraints are sometimes cross-dimensional. Recently, the Swarm Intelligence Based (SIB) method is proposed for problems in discrete domains, and it is widely applied in many mathematical and statistical problems that common metaheuristic methods seldom approach. In this work, we introduce an extension of the SIB method that handles solutions with many dimensions, or tensor solution in mathematics. We further speed up our method by implementing our algorithm with the use of CPU parallelization. We then apply this extended framework to real applications in designing selling scheme, showing that our proposed method helps to increase the profit of a selling scheme compared to those suggested by traditional methods.
AB - Nature-inspired metaheuristic optimization has been widely used in many problems in industry and scientific investigations, but their applications in designing selling scheme are rare because the solution space in this kind of problems is usually high-dimensional, and their constraints are sometimes cross-dimensional. Recently, the Swarm Intelligence Based (SIB) method is proposed for problems in discrete domains, and it is widely applied in many mathematical and statistical problems that common metaheuristic methods seldom approach. In this work, we introduce an extension of the SIB method that handles solutions with many dimensions, or tensor solution in mathematics. We further speed up our method by implementing our algorithm with the use of CPU parallelization. We then apply this extended framework to real applications in designing selling scheme, showing that our proposed method helps to increase the profit of a selling scheme compared to those suggested by traditional methods.
KW - CPU parallelization
KW - Selling scheme
KW - Swarm intelligence
KW - Tensor-type particle
UR - http://www.scopus.com/inward/record.url?scp=85112073718&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78743-1_7
DO - 10.1007/978-3-030-78743-1_7
M3 - Conference contribution
AN - SCOPUS:85112073718
SN - 9783030787424
T3 - Lecture Notes in Computer Science
SP - 72
EP - 82
BT - Advances in Swarm Intelligence. ICSI 2021
A2 - Tan, Ying
A2 - Shi, Yuhui
PB - Springer
T2 - 12th International Conference on Advances in Swarm Intelligence 2021
Y2 - 17 July 2021 through 21 July 2021
ER -