A Double Mutation Cuckoo Search Algorithm for Solving Systems of Nonlinear Equations
Chiwen Qu, Wei He · International Journal of Hybrid Information Technology · 2015
This paper presents a double mutation cuckoo search algorithm (DMCS) to overcome the disadvantages of traditional cuckoo search algorithms, such as bad accuracy, low convergence rate, and easiness to fall into local optimal value. The algorithm mutates optimal fitness parasitic nests using small probability, which enhances the local search range of the optimal solution and improves the search accuracy. Meanwhile, the algorithm uses large probability to mutate parasitic nests in poor situation, which enlarges the search space and benefits the global convergence. The experimental results show that the algorithms can effectively improve the convergence speed and optimization accuracy when applied to basic test functions and systems of nonlinear equations.