Group search optimizer (GSO) is a stochastic, population-based optimization technique that has shown better performance as for global searching when optimizing multimodal benchmarks. However, it suffers from poor convergence because of its producer-scrounger model, which makes it unable to get a reliable estimiator for evolution path. In order to enhance the local search ability without impairing the global seach ability of GSO, this paper proposes a variant of group search optimizer (VGSO), which develops a producer-organizer model whereby group members are assumed to search for opportunities of either `finding' (producing) or `learning' (organization). This model enables VGSO to achieve a good trade-off between its exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions and a practical optimization problem. Comparison results show that VGSO obtains a promising performance on these optimization problems.
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