竞选优化算法实现及其仿真研究

摘要

In solving complicated optimization problems, the key is to find a suitable optimal algorithm. Traditional numerical methods are sensitive to the initial parameter and need differential coefficient of functions. Many modern optimization algorithms often converge at local optimal value and have deficiencies in convergence speed and calculation efficiency. Election-Survey Algorithm(ESA), a new intelligent optimization algorithm by simulating the motivation of election candidates to obtain the higher support ratio in campaign, is introduced, and the principle and step of the ESA are given in the paper. In order to demonstrate the practicality andeffectiveness of the algorithm, the test functions are simulated and analyzed with the ESA. The Simulation results of one and multi-dimensional typical function optimization problems show that the ESA can converge to global optimal solution rapidly and is of better stability.%研究寻找一种合适的优化算法是求解复杂优化问题成功的关键.传统数值方法对初始值的选择很敏感并需要对函数求导,许多现有的现代优化算法则存在容易陷人局部最优解或收敛速度较慢、计算效率较低的问题.一种新的智能优化竞选算法是模拟人类竟选活动中追求更高支持率所蕴涵的优化思想而建立的一种新的启发式优化算法.首先提出了算法的原理和实现步骤.然后为了验证算法的可行性和有效性,运用典型的测试函数进行仿真研究.一维和多维典型函数优化问题的仿真测试结果表明,竞选算法能够快速收敛于全局最优解,并具有较好的稳定性.