Cooperative Optimization Algorithm for the 100-Digit Challenge

Zhang, Geng1; Shi, Yuhui2; Steed Huang, Jun2

10.1109/CEC.2019.8790272

2019

978-1-7281-2154-3

2019 IEEE Congress on Evolutionary Computation, CEC 2019

376-380

10-13 June 2019

Wellington, New zealand

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

As for optimization algorithms, the local optima of multimodal problems always bring difficulties of finding global optimum. This paper proposes a cooperative optimization method that combines differential evolution (DE), univariate sampling, harmony search (HS) and particle swarm optimizer (PSO) to deal with the 100-Digit Challenge of finding global optimum of multimodal problems. The basic strategy is to use some effective algorithms such as DE and univariate sampling to obtain several local optimal solutions, and then to explore a promising search space represented by these local solutions to try to find the global optimum. Experimental tests demonstrate the effectiveness of the proposed cooperative method.
© 2019 IEEE.

multimodal problems univariate sampling differential evolution

其他

英语

EI ; CPCI-S

Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284]

Engineering ; Mathematical & Computational Biology

Engineering, Electrical & Electronic ; Mathematical & Computational Biology

WOS:000502087100051

20193507374036

Computer science ; Evolutionary algorithms

Computer Software, Data Handling and Applications:723

EV Compendex

1.School of Computer and Information Technology, Nanyang Normal University, Nanyang, China
2.Shenzhen Key Laboratory of Computational Intelligence, Southern University of Science and Technology, Shenzhen, China

Zhang, Geng,Shi, Yuhui,Steed Huang, Jun. Cooperative Optimization Algorithm for the 100-Digit Challenge[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:376-380.