基于模拟退火的花朵授粉优化算法

针对花朵授粉算法寻优精度低、收敛速度慢、易陷入局部极小的不足,提出一种把模拟退火(SA)融入到花朵授粉算法中的混合算法。该算法通过SA的概率突跳策略使其避免陷入局部最优,并利用SA的全域搜索的性能增强算法的全局寻优能力。通过6个标准测试函数进行测试,仿真结果表明,改进算法在4个测试函数中能够找到理论最优值,其收敛精度、收敛速度、鲁棒性均比基本的花朵授粉算法(FPA)、蝙蝠算法(BA)、粒子群优化(PSO)算法及改进的粒子群算法有较大的提高;同时,对非线性方程组问题进行求解的算例应用也验证了改进算法的有效性。

Flower pollination algorithm based on simulated annealing

A hybrid algorithm of Simulated Annealing (SA) and flower pollination algorithm was presented to overcome the problems of low-accuracy computation, slow-speed convergence and being easily relapsed into local extremum. The sudden jump strategy in SA was utilized to avoid falling into local optimum, and the global searching performance of SA was exploited to enhance the global searching ability of the hybrid algorithm. The hybrid algorithm was tested through six standard functions and compared to basic Flower Pollination Algorithm (FPA), Bat Algorithm (BA), Particle Swarm Optimization (PSO) algorithm and improved PSO algorithm. The simulation results show that the optimal value of 4 functions were found by the hybrid algorithm with better convergence precision, convergence rate and robustness. At the same time, the experimental results of solving nonlinear equation group verify the validity of the hybrid algorithm.