Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies

Li HUANG  Xiao ZHENG  Shuai DING  Zhi LIU  Jun HUANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.10   pp.1916-1924
Publication Date: 2019/10/01
Publicized: 2019/07/09
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7013
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
multi-learning strategy,  cuckoo search,  bound handling mechanism,  elite learning,  

Full Text: FreePDF

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability Pc. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.