【摘要】 提出一种将单纯形法SM与粒子群算法PSO混合的新颖优化算法,在10个著名测试函数上与其他已有算法进行了广泛的比较实验,并研究了不同参数选择对算法的影响.实验结果表明,这种混合算法对传统PSO求解的收敛率和解的质量有较明显的改善,在多峰函数优化问题上优势更突出.算法实现简单,具有很高的可靠性,是一种求解多峰连续函数极值的有效方法.*更多还原

【Abstract】 A novel hybrid simplex method and particle swarm optimization(HSMPSO) algorithm is presented.Comparison experiments with other published methods on 10 benchmark functions are conducted,and the extensive(analysis) on the effect of different parameters on the algorithm is given.Experimental results indicate that the hybrid method can improve both solution quality and success rates on most selected test functions compared with other alternatives,especially on multimodal functions optimization.Although very easy to implement,this hybrid SM-PSO is an efficient way to locate global optima of continuous multimodal functions.更多还原