An Enhanced EDBF Framework: Constraint-Law-Method (CLM) for Improving Multi-parent Crossover Algorithms

Abstract Empirical distribution-based framework (EDBF), as a general framework utilized in lots of muti-parent crossover algorithms (MCAs), makes MCAs much more efficient at each iteration. However, EDBF cannot work with the numerous parent chromosomes especially exceeding fifty. To address this problem, an enhanced EDBF framework, namely constraint-law-method (CLM), is proposed by adaptively changing the boundary of weight assigned to each parent chromosome according to the constraint law. Furthermore, CLM is compared with EDBF, ABPSO and RCBBFA algorithms on 20 benchmark functions in terms of convergence, efficiency and accuracy. Experimental results demonstrate that CLM outperforms comparative algorithms on most of benchmark functions. As a general framework rather than a specific algorithm, CLM is easy to implement and can easily be accommodated to any existing MCAs. Finally, the C++ source code is available at https://github.com/ZhengkangZUO-2020/CLM-Framework-Codes .