针对传统粒子群优化算法容易陷入局部最优、寻优精度低及后期搜索速度慢等缺陷,提出一种参考局部最差解影响的粒子群算法.当算法搜索后期,全局最优解(global best solution,Gbest)无变化时,局部最优解(personal best solution,Pbest)等...针对传统粒子群优化算法容易陷入局部最优、寻优精度低及后期搜索速度慢等缺陷,提出一种参考局部最差解影响的粒子群算法.当算法搜索后期,全局最优解(global best solution,Gbest)无变化时,局部最优解(personal best solution,Pbest)等于Gbest,这时速度靠拢最优方向向量为零,粒子前进的方向只有自身惯性.而本文加入了局部最差(partial worst solution,Pworst)之后的算法使粒子的前进方向不仅受自身惯性的影响,而且可以继续的寻优,从而找到Gbest.算法采用远离全局最差解和局部最差解的思想,对粒子群优化算法的速度更新公式进行改进,并分别测试全局最差解和局部最差解对粒子群优化算法的影响.通过几个典型的测试函数仿真结果表明,改进后的算法在搜索速度、寻优精度、鲁棒性方面较粒子群算法有了显著提高,而且具有跳出局部最优的能力.展开更多
The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support v...The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.展开更多
文摘针对传统粒子群优化算法容易陷入局部最优、寻优精度低及后期搜索速度慢等缺陷,提出一种参考局部最差解影响的粒子群算法.当算法搜索后期,全局最优解(global best solution,Gbest)无变化时,局部最优解(personal best solution,Pbest)等于Gbest,这时速度靠拢最优方向向量为零,粒子前进的方向只有自身惯性.而本文加入了局部最差(partial worst solution,Pworst)之后的算法使粒子的前进方向不仅受自身惯性的影响,而且可以继续的寻优,从而找到Gbest.算法采用远离全局最差解和局部最差解的思想,对粒子群优化算法的速度更新公式进行改进,并分别测试全局最差解和局部最差解对粒子群优化算法的影响.通过几个典型的测试函数仿真结果表明,改进后的算法在搜索速度、寻优精度、鲁棒性方面较粒子群算法有了显著提高,而且具有跳出局部最优的能力.
基金Project 072400430420 supported by the Natural Science Foundation of Henan Province
文摘The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.
