Abstract
Biogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many optimization problems due to its simplicity and efficiency. However, BBO is sensitive to the curse of dimensionality; its performance degrades rapidly as the dimensionality of the search space increases. In this paper, a selective migration operator is proposed to scale up the performance of BBO and we name it selective BBO (SBBO). The differential migration operator is selected heuristically to explore the global area as far as possible whist the normal distributed migration operator is chosen to exploit the local area. By the means of heuristic selection, an appropriate migration operator can be used to search the global optimum efficiently. Moreover, the strategy of cooperative coevolution (CC) is adopted to solve large-scale global optimization problems (LSOPs). To deal with subgroup imbalance contribution to the whole solution in the context of CC, a more efficient computing resource allocation is proposed. Extensive experiments are conducted on the CEC 2010 benchmark suite for large-scale global optimization, and the results show the effectiveness and efficiency of SBBO compared with BBO variants and other representative algorithms for LSOPs. Also, the results confirm that the proposed computing resource allocation is vital to the large-scale optimization within the limited computation budget.
Highlights
Evolutionary algorithms (EAs) are efficient tools to solve complex optimization problems
Due to the dimensionality mismatch brought by divide and conquer (DC), which implies that the subsolution cannot be evaluated by the original objective function directly, it is a natural way to complement the subsolution to be evaluated as a complete solution by the combination of the representative of each subproblem, known as cooperative coevolution (CC)
To deal with the large-scale global optimization problems (LSOPs) in the context of DC, we propose to use selective BBO (SBBO) as the base optimizer and allocate the computing resource to different subcomponents according to the Relative Fitness Improvement (RFI)
Summary
Evolutionary algorithms (EAs) are efficient tools to solve complex optimization problems. The DC framework is more efficient and more popular Recent works along this line mainly focus on the grouping strategy for subproblem division, e.g., random grouping [17] and recursive differential grouping [18]; on the other hand, the performance of optimizers and the allocation of computing resources among subproblems within limited computational budget are crucial but have not been largely explored yet. We intend to scale up the performance of BBO to solve the LSOPs. We propose a novel Selective Migration Operator (SMO) to balance exploration and exploitation.
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