A fast hybrid algorithm for global optimization
A fast hybrid algorithm for global optimization
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一种用于全局优化的快速混合算法
DOI：
10.1109/icmlc.2005.1527462
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Yu
中科院分区：
文献类型：
--
作者：
Yong;Jiangshe Zhang;Yu
提出了一种结合梯度下降法和粒子群优化算法的全局优化算法。采用梯度下降技术快速有效地找到目标函数的局部极小值，粒子群算法帮助极小化序列从先前收敛的局部极小值逃逸到一个更好的点。该算法通过反复搜索直至找到目标函数的全局最小值，并引入排斥技术和部分初始化种群方法。对基准问题的测试表明，该方法比现有的优化方法更有效、更可靠。
An algorithm, consisting of gradient descent technique and particle swarm optimization (PSO) method for global optimization is proposed. The gradient descent technique is used to find a local minimum of objective function fast and efficiently, and particle swarm optimization method helps minimization sequence to escape from the previously converged local minima to a better point. The search procedure is applied repeatedly till a global minimum of the objective function is found. In addition, a repulsion technique and partially initializing population method are also incorporated in the new algorithm. Global convergence is proven, and test on benchmark problems shows that the proposed method is more effective and reliable than the existed optimization methods.