Number Theoretic Global Optimization Searching Algorithm Based On Evolution

摘要

When the sequential number theoretic optimization algorithm based on reference book is used for solving global optimum solution of the continuous multiple hump function, the causes that algorithm falls easily into local optimum solution are analyzed. By the functions of the evolution algorithm global searching and local microadjusting, combining the sequential number theoretic optimization algorithm and evolution algorithm, an evolutionary number theoretic global optimization searching algorithm is established. A fast effective algorithm is provided for finding the global optimal point of a continuous multi-extreme function. The convergence of the algorithm is analyzed and numerical examples are presented.