Neighborhood Learning-Based Cuckoo Search Algorithm for Global Optimization

This paper presents a new variant of cuckoo search (CS) algorithm named neighborhood learning-based CS (NLCS) to address global optimization problems. Specifically, in this modified version, each individual learns from the personal best solution rather than the best solution found so far in the entire population to discourage premature convergence. To further enhance the performance of CS on complex multimode problems, each individual is allowed to learn from different learning exemplars on different dimensions. Moreover, the exemplar individual is chosen from a predefined neighborhood to further maintain the population diversity. This scheme enables each individual to interact with the historical experience of its own or its neighbors, which is controlled by using a learning probability. Extensive comparative experiments are conducted on 39 benchmark functions and two application problems of neural network training. Comparison results indicate that the proposed NLCS algorithm exhibits competitive convergence performance.