基于非线性搜索策略的改进灰狼优化算法及其应用

Improved Gray Wolf Optimization Algorithm and Its Application with Nonlinear Search Strategies

灰狼算法(GWO)在大工业复杂的优化问题求解中存在许多不足之处,如容易陷入局部最优解、收敛速度慢、最优解精度低等缺点,由此提出一种改进的灰狼优化算法.在灰狼算法的基础上,通过引入非线性参数和新的位置迭代更新方程,构建一种基于非线性策略的灰狼优化算法,并通过软件实现算法.通过8个标准测试函数在多维度下的数值对比实验和一个工程设计优化问题求解,分析验证算法的稳定性和优越性,并证明其性能均优于原有的GWO系列算法,是一种具有潜力的元启发式算法.

The gray wolf algorithm(GWO)has many shortcomings in solving complex optimization problems in large industries,such as easy-to-fall into local optimal solutions,slow convergence speed,low accuracy of optimal solutions,and other shortcomings.Thus an improved Gray wolf optimization algorithm is proposed.Based on the gray wolf algorithm,a gray wolf optimization algorithm based on a nonlinear strategy is constructed by introducing nonlinear parameters and new position iterative updating equations,and the algorithm is implemented by software.The stability and superiority of the algorithm are analyzed and verified through numerical comparison experiments of eight standard test functions in multiple dimensions and the solution of an engineering design optimization problem.It is proved that its performance is better than that of the original GWO series algorithms,which is a metaheuristic algorithm with potential.