A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems
A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems
复制标题
一种解决无约束优化问题的新型猫群优化算法
DOI：
10.5815/ijitcs.2013.11.04
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Teshnehlab
中科院分区：
文献类型：
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作者：
Meysam Orouskhani;Yasin Orouskhani;M. Mansouri;M. Teshnehlab
猫群优化算法是一种新型的群体智能算法，用于寻找全局最优解。由于复杂性，有时纯CSO需要很长时间才能收敛，并且不能获得精确解。为了解决这个问题，提高收敛精度水平，我们提出了一种新的改进的CSO，即“自适应动态猫群优化”。首先在速度方程中加入一个新的自适应惯性权重，然后采用一个自适应加速度系数。其次，利用前/后两维信息，通过引入一个新的因子，得到了一个新的位置更新方程，该方程由位置和速度信息的平均值组成。对6个测试函数的实验结果表明，与纯CSO相比，该算法收敛时间短，能在较少的迭代次数内找到最优解。
Cat Swarm Optimization (CSO) is one of the new swarm intelligence algorithms for finding the best global solution. Because of complexity, sometimes the pure CSO takes a long time to converge and cannot achieve the accurate solution. For solving this problem and improving the convergence accuracy level, we propose a new improved CSO namely 'Adaptive Dynamic Cat Swarm Optimization'. First, we add a new adaptive inertia weight to velocity equation and then use an adaptive acceleration coefficient. Second, by using the information of two previous/next dimensions and applying a new factor, we reach to a new position update equation composing the average of position and velocity information. Experimental results for six test functions show that in comparison with the pure CSO, the proposed CSO can takes a less time to converge and can find the best solution in less iteration.