一种具有学习机制的海鸥优化算法

An Improved Seagull Optimization Algorithm with Learning

为了克服海鸥优化算法在求解高维问题时存在的收敛速度慢、容易早熟和解精度低等问题,提出一种具有学习机制的海鸥优化算法(ISOAL)。首先,设计了一种基于当前粒子X_(i)与种群均值状态X_(m)差异的迁移算子,提升早期个体对解空间的搜索范围。其次,引入非线性自适应参数A保证算法适合于复杂问题解空间的搜索,避免算法过早地陷入局部最优。最后,通过引入部分精英粒子执行反向学习,加强对种群内的最优粒子所在空间的勘探,提高算法的解精度。实验选择了CEC2017中的10个无约束测试函数检测算法的性能,并与HPSO-TS、V-DVGA、DADE、CMA-ES等算法进行对比,该组实验结果显示,ISOAL比其他算法具有更高的解精度和稳定性。针对张力弹簧问题进行实验,结果表明:ISOAL所获得的弹簧总代价比SOA降低了3.5%,弹簧的线圈直径和平均直径分别下降了5.7%和3.5%。ISOAL算法具有收敛速度快、精度高和鲁棒性的特点,适合求解较高维度的连续函数优化问题和带有约束的工程优化问题。

To overcome the weakness of slow convergence,prematureness and low accuracy of seagull optimization algorithm(SOA)in solving high-dimensional problems,an improved SOA with learning(ISAOL)was proposed.A migration operator based on the difference between the X_(i) and the X_(m) was designed,this could make X_(i) search wider solution spaces in early stage,and a nonlinear adaptive parameter A was introduced to ensure the algorithm suitable for the search of solution space of complex problems,which could prevent the algorithm from falling into local optimum too early.In the later stage,some elite individuals executed opposition based learning(OBL)to intensify the exploration of the space around the global optimal individual to improve the accuracy of solution.Ten unconstrained test functions in CEC2017 were selected to test the performance of the ISOA and compared with HPSO-TS,V-DVGA,DADE,CMA-ES and others.Testing results of this experiments showed that the ISOAL had higher accuracy and stability than other algorithms.Finally,experiments was carried out by using the tension spring problem.The results showed that the total cost of the spring,the coil diameter and the average diameter of the spring obtained by the ISOAL were reduced by 3.5%,5.7%and 3.5%than SOA,respectively.ISOAL had the attributes of fast convergence,high accuracy and robustness,fitting to solve higher dimensional function optimization problem and engineering optimization problems with constraints.