求解约束优化问题改进的水波优化算法

Improved water wave optimization for constrained optimization problems

为提高约束优化模型的求解精度,提出一种改进的水波优化算法。设计主-从异构种群,结合ε约束处理技术使主群实现探索可行解,从群利用可行解搜寻全局最优解。为加快收敛速度和增强信息交互,主群中个体可以依概率进行个体间学习,设计水波波长函数,使其随着水波的适应度值和违反约束度及时调整。为避免早期收敛,从群采用自适应学习策略以平衡群体的探索和利用。设计随迭代次数变化的放松约束度,提高算法收敛精度。对比实验结果表明,该算法可以获得高质量的可行解。

An improved water wave optimization(IWWO)was presented to improve the solution accuracy for complicated constrained optimization problems.In the IWWO,the heterogeneous master-slave population structure withεconstraint handling technique was designed to achieve that the master swarm searched feasible solutions and the slave population found the global optimum using the feasible solutions.The individual in the maser population could learn from the others according to probability to strengthen information interaction between individuals and to enhance the convergence speed.Meanwhile,a wavelength function of each wave was designed in which the wavelength was adjusted with the evolutionary state of the water wave.In the slave swarm,an adaptive learning strategy was developed to prevent the swarm getting into the premature convergence.The relaxation degree of constraint violation varied with iterations,which improved the convergence precision.Experimental results show that the proposed algorithm is capable of obtaining high-quality solutions compared with some other state-of-the-art constrained optimization algorithms.