An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution (WCSDE) to overcome the shortcomings of CS algorithm, such as low convergence accuracy, lack information exchange within population, inadequate local capabilities. Compared with other variants, two strategies are proposed in this improve properties WCSDE. On one hand, a non-linearly decreasing inertia number evolutionary iterations is employed WCSDE update method bird’s nest position, enhance balance between exploration development capabilities, strengthen optimization capability. mutation cross-selection mechanisms (DE) introduced make up for mutual relationship populations, avoid loss practical information, increase accuracy. In experiment part, 13 classic benchmark functions selected execute function tasks among standard CS, WCSDE, four variants verify effectiveness from aspects. The results corresponding statistical analysis reveal that has better global ability strengthener robustness.