Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

Kazimipour, Borhan1; Omidvar, Mohammad Nabi2; Qin, A. K.3; Li, Xiaodong1; Yao, Xin2,4

2019-03

10.1016/j.asoc.2018.12.007

APPLIED SOFT COMPUTING   影响因子和分区

1568-4946

1872-9681

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple banditbased credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.

Imbalanced large-scale optimization Resource allocation Multi-armed bandits

SCI ; EI

英语

其他

Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000461145200019

ELSEVIER SCIENCE BV

20185206317260

Optimization

Management:912.2 ; Optimization Techniques:921.5

COMPUTER SCIENCE

Web of Science

1.Sch Sci Comp Sci & Software Engn, Melbourne, Vic 3000, Australia
2.Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
3.Swinburne Univ Technol, Dept Comp Sci & Software Engn, John St, Hawthorn, Vic 3122, Australia
4.Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China

Kazimipour, Borhan,Omidvar, Mohammad Nabi,Qin, A. K.,et al. Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems[J]. APPLIED SOFT COMPUTING,2019,76:265-281.

Kazimipour, Borhan,Omidvar, Mohammad Nabi,Qin, A. K.,Li, Xiaodong,&Yao, Xin.(2019).Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems.APPLIED SOFT COMPUTING,76,265-281.

Kazimipour, Borhan,et al."Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems".APPLIED SOFT COMPUTING 76(2019):265-281.