Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems

Abstract

Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to enhance the local search exploitation and convergence speed. It uses the parent’s optimal and worst group solutions to guide the next iteration. Finally, a novel spatial compression (SC) method is applied to the algorithm to accelerate the speed of iteration. It compresses the convergence space at an appropriate time, which is aimed at improving the shrinkage speed of the algorithm. AGSCCS has been examined on a suite from CEC2014 and compared with the traditional CS, as well as its four latest variants. Then the parameter identification and optimization of the photovoltaic (PV) model are applied to examine the capacity of AGSCCS. This is conducted to verify the effectiveness of AGSCCS for industrial problem application.