Abstract
Since coming out, novel composition test functions have received wide attention from evolutionary computation researchers and have now become the target functions for numerical optimization algorithms. However, its numerical optimization can be transformed into numerical optimization of one-dimensional functions, which significantly reduces optimization level of difficulty. A novel composition test functions algorithm for numerical optimization is proposed, which quotes a muti-population coevolutionary algorithm for numerical optimization and uses it to optimize the one-dimensional functions. The experiments proved the algorithm for numerical optimization of novel composition test functions converges to the global optimal solutions.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
