Bi-space Interactive Cooperative Coevolutionary algorithm for large scale black-box optimization

Abstract

Large scale black-box optimization problems arise in many fields of science and engineering, and many of existing algorithms for these problems still suffer from the “curse of dimensionality”. This paper proposes a generalized framework of Bi-space Interactive Cooperative Coevolutionary Algorithm (BICCA) with evolutions in two spaces. In the pattern space, the interacting patterns of variables are continuously excavated for the evolution of the groups for cooperative coevolution. In the search space, cooperative coevolution and global search are carried out adaptively to get better fitness. By adopting evolutions and interactions within two spaces, patterns evolve to provide better groupings while individuals evolve to reach better fitness. The problem decomposition is conducted along the optimization process, and no extra fitness evaluations are needed for problem decomposition. Experiments on widely-used benchmarks show that BICCA obtains competitive performance on high-dimensional optimization problems with different levels of dimensionality up to 10000.