本文将均匀搜索粒子群算法(Uniform search Particle Swarm Optimization,简称UPS0)的位置更新公式变换为一个差分方程,求解差分方程得到非递推的位置更新公式,推导解的收敛条件并求出了UPSO对学习系数c及惯性系数w的收敛区域,最后通过6个Benchmark 函数仿真实验对收敛区域的正确性进行验证,实验结果表明学习系数和惯性系数在收敛区域内时的UPSO收敛,不在收敛区域外时UPS0发散.%The uniform search particle swarm optimization (UPSO) algorithm formula was tsansformed into a differential e-quation. Solving the differential equation, we get a non-recurrence location update formula, and the UPSO's convergence region for learning coefficient c and inertia coefficient w were concluded by deducing the solution convergence conditions.Finally simulation experiments were provided on the selected location of the region of convergency by 6 Benchmark functions. Experimental results show that UPSO converges when the learning coefficient and inertial coefficient are in the convergence region and diverge outside convergence region.
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