Bi-Hierarchical Cooperative Coevolution for Large Scale Global Optimization

Taking 'divide-and-conquer' as a basic idea, cooperative coevolution (CC) has shown a promising prospect in large scale global optimization. However, its high requirement on the decomposition accuracy can hardly be satisfied in practice. Directing against this issue, this study proposes a bi-hierarchical cooperative coevolution (BHCC), which can tolerate a certain degree of decomposition error. Besides the cooperation among sub-problems as in the conventional CC, BHCC introduces a kind of cooperation between sub-problems and the overall problem. By systematically exploiting the excellent sub-solutions obtained during the sub-space optimization process, it initializes the population for the optimization process on the overall problem and thus can conduct search in promising regions of the whole solution space. The newly acquired complete solutions are in turn employed to update the context vector and the population of each sub-problem, where the context vector is used for sub-solution evaluation. Consequently, the search direction misdirected by an improper decomposition can be corrected to a great extent. To keep the balance between the two types of optimization processes, an adaptive triggering mechanism for the overall optimization process is specially designed for BHCC. Experimental results on two widely-used benchmark suites verify the effectiveness of the new strategies in BHCC and also indicate that BHCC is more robust than existing CCs and can achieve competitive performance compared with several state-of-the-art algorithms.