Improved GuoTao Algorithm for Unconstrained Optimization Problems

This paper presents an improved GuoTao algorithm for solving unconstrained optimization problems. The algorithm combines the multi-parent crossover operator of GuoTao algorithm with two local search operators. The first local search operator is a simplified Hooke-Jeeves algorithm which can find near-optimal solutions near the start point within reasonable computational time. And the second local search operator is a powell algorithm which can find the local optimal solution near the start point. The multi-parent crossover operator can enables individual to draw closer to each local optimal solution, thus the population will be divided into subpopulations automatically, meanwhile, the first local search operator is adopted to gain near-optimal solution in each subpopulation. As a result, the promising areas are gained quickly which decrease useless search greatly. At last, the second local search operator is adopted to find the global optimal solution only from those promising areas.Numerical experiments using a suite of test functions, which are widely studied in the field of evolutionary computation, show that the proposed algorithm is superior to GuoTao algorithm for solving unconstrained optimization problems with high dimensionality.