Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization

Zhang，Youkui1; Duan，Qiqi2; Shao，Chang3; Shi，Yuhui1

10.1109/SSCI50451.2021.9659957

2021

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

978-1-7281-9049-5

2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

01-07

5-7 Dec. 2021

Orlando, FL, USA

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

IEEE

In this paper, we present a simple yet efficient parallel version of simulated annealing (SA) for large-scale black-box optimization within the popular population-based framework. To achieve scalability, we adopt the island model, commonly used in parallel evolutionary algorithms, to update and communicate multiple independent SA instances. For maximizing efficiency, the copy-on-write operator is used to avoid performance-expensive lock when different instances exchange solutions. For better local search ability, individual step sizes are dynamically adjusted and learned during decomposition. Furthermore, we utilize the shared memory to reduce data redundancy and support concurrent fitness evaluations for challenging problems with costly memory consumption. Experiments based on the powerful Ray distributed computing library empirically demonstrate the effectiveness and efficiency of our parallel version on a set of 2000-dimensional benchmark functions (especially each is rotated with a 2000*2000 orthogonal matrix). To the best of our knowledge, these rotated functions with a memory-expensive data matrix were not tested in all previous works which considered only much lower dimensions. For reproducibility and benchmarking, the source code is made available at https://github.com/Evolutionary-Intelligence/PPSA.

Decomposition Evolutionary algorithms Global optimization Parallel computing Simulated annealing

第一

英语

CPCI-S ; EI

Shenzhen Fundamental Research Program[JCYJ20200109141235597]

Computer Science ; Engineering ; Operations Research & Management Science ; Mathematics

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science ; Mathematics, Applied

WOS:000824464300138

20221011761136

Efficiency ; Evolutionary algorithms ; Global optimization ; Matrix algebra

Heat Treatment Processes:537.1 ; Production Engineering:913.1 ; Algebra:921.1 ; Optimization Techniques:921.5

2-s2.0-85125797415

Scopus

1.Southern University of Science and Technology (SUSTech),Shenzhen,China
2.Harbin Institute of Technology (HIT),Harbin,China
3.University of Technology Sydney (UTS),Sydney,Australia

Zhang，Youkui,Duan，Qiqi,Shao，Chang,et al. Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:01-07.