A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor

Abstract

Existing multi-objective cuckoo search (MOCS) algorithms are based on either Pareto dominance or decomposition. However, when dealing with complex multi-objective problems (MOPs), the Pareto dominance-based algorithms face a decrease in selection pressure, and the decomposition-based algorithms easily gain poor distributions. The objective of this paper is to repurpose an indicator-based MOCS by combining improved diversity enhancement (IDE) and adaptive scaling factor (ASF) for MOPs. In the proposed algorithm, hypervolume is used as the indicator to guarantee better convergence and enough spread of the population. IDE chooses the large hypervolume to rebuild the parent population to compensate for the lack of population diversity. Additionally, ASF makes full use of individuals information to enhance the search ability of Lévy component in cuckoo search. Comprehensive experiments on 31 benchmark functions including two classical suites ZDT, WFG, and one challenged suite proposed in CEC2019, as well as 8 real-world problems were conducted to test the proposed algorithm. Compared with several state-of-the-art multi-objective evolutionary algorithms, the effectiveness and efficiency of our proposed method were demonstrated by the results.