针对高维复杂优化问题,提出一种改进适应度函数和动态调整惯性权重的粒子群优化算法.首先考虑了搜索点的函数值及其变化率,并将该信息加入适应度函数.利用维惯性权重矩阵自适应动态调整惯性权重,较好地平衡了算法的全局探索和局部开发,并分析了惯性权重随种群多样性的变化关系.在算法后期计算每一维的收敛度,以一定的概率对收敛度最小的维进行变异,以加快算法的收敛速度.对高维测试函数的实验表明,算法提高了全局搜索能力.%A novel particle swarm optimization (NPSO) with modified fitness function and dynamic change of inertia weights was proposed for solving complex high-dimensional optimization problems. In this algorithm, both the function value at the searching point and the function change rate at the point were combined into fitness function. This new approach could balance the local searching and the global searching by adopting inertia weight matrix to adaptively and dynamically adjust inertia weights. The convergence degree of every dimension was calculated and the dimension of minimal convergent degree was mutated according to some probability. Experiments on five high-dimension test functions indicate that NPSO can enhance the performance of global searching.
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