Multivariant Optimization Algorithm (MOA) is proposed to effectively solve complex multimodal optimization problems through tracking the history information by multiple variant search groups based on a structure. The proposed method has the ability to locate optimum through global-local search iterations which are carried out by a global exploration group and local exploitation groups which are not only multiple but also variant. In this paper, we study the unbiasedness property of MOA and prove that MOA provides an unbiased estimate of the optimal solution for identification problem on an AR model where the outputs are corrupted by noises. The comparison experiments on the identifications of AR model by (Finite Impulse Response) FIR filter shows that MOA is superior to recursive least squares (RLS) and the particle swarm optimization (PSO) in unbiasedness property.
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