When Cooperative Co-Evolution Meets Coordinate Descent: Theoretically Deeper Understandings and Practically Better Implementations

Duan, Qiqi1; Shao, Chang1; Qu, Liang1; Shi, Yuhui1; Niu, Ben2

10.1109/CEC.2019.8790148

2019

978-1-7281-2154-3

2019 IEEE Congress on Evolutionary Computation, CEC 2019

721-730

10-13 June 2019

Wellington, New zealand

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

Institute of Electrical and Electronics Engineers Inc.

Decomposition-based optimizers have shown very promising computational and convergence performance on many large-scale real-parameter optimization problems. Among them, a class of recently proposed cooperative coevolutionary algorithms (CCEAs) and a type of conventional block coordinate descent algorithms (BCDAs) are arguably the two most representative frameworks applied to the minimization of non-differentiable and differentiable objective function, respectively. This paper explores the connections between CCEAs and BCDAs, which can help gain deeper understandings of CCEAs. First, we propose a unified analytical framework for both CCEAs and BCDAs to capture the common game-theoretic nature by combining their respective theoretical advances. Second, many real-world objective functions are non-additively separable, where all decision variables interact with each other in a direct or indirect fashion. However, most of the state-of-the-art decomposition strategies for CCEAs can only capture the simple additive separability and cannot recognize the non-additive separability, but which has been widely studied in the BCDAs context. The performance of CCEAs on such functions is yet to be fully understood since intuitively CCEAs seem to be not suitable for them. We use the proposed framework to confirm and extend CCEAs' applicability to a special class of non-additively separable functions. Finally, based on the proposed framework, we provide two practical suggestions as well as a suite of new test functions to help design practically better CCEAs for large-scale optimization.
© 2019 IEEE.

cooperative co-evolution coordinate descent divide and conquer Nash equilibria non-additive separability

第一 ; 通讯

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:000502087100096

20193507373882

Additives ; Evolutionary algorithms ; Game theory

Chemical Agents and Basic Industrial Chemicals:803 ; Optimization Techniques:921.5 ; Probability Theory:922.1

EV Compendex

1.Shenzhen Key Laboratory of Computational Intelligence, Southern University of Science and Technology, Shenzhen, China
2.College of Management, Shenzhen University, Shenzhen, China

Duan, Qiqi,Shao, Chang,Qu, Liang,et al. When Cooperative Co-Evolution Meets Coordinate Descent: Theoretically Deeper Understandings and Practically Better Implementations[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:721-730.