A large-scale continuous optimization benchmark suite with versatile coupled heterogeneous modules

Abstract

The complexity of the engineering design optimizations mainly sources from the large-scale nature of the problems, and the ever-increasing large-scale global optimization (LSGO) problems place high demands on the performance of evolutionary algorithms (EAs). To compare and analyze large-scale optimization algorithms, benchmarks that simulate the features from real-world large-scale optimization are desired. In this paper, we propose a novel large-scale continuous optimization benchmark suite, which includes 15 test functions with the modular structure. The proposed benchmark suite introduces two new features abstracted from the engineering design problems: (1) Heterogeneous design, i.e., the modules are significantly different in terms of difficulty and explicit expressions. (2) Versatile coupling, i.e., different coupling degrees and random coupling topologies of the benchmark are emphasized. Two typical cooperative coevolution frameworks and several state-of-the-art large-scale optimization algorithms are used for comparative studies on the proposed benchmark, and experimental results demonstrate that the proposed benchmark suite is very challenging.