Abstract: Clouds have many natural phenomena such as generation, dynamic movement, rainfall and regeneration. A novel intelligent optimization algorithm called clouds search optimization algorithm, or CSO was proposed by blending these natural phenomena of clouds with the ideas of intelligent optimization algorithms. Droplets inside a cloud can produce difference quotient information to guide the search. Difference quotient information can approximate gradient, and its reverse direction can guide function value's decline. On the basis of difference quotient's those properties, clouds search optimization algorithm with difference quotient information (DCSO) was also proposed. It proved to be convergent by using the relationship between difference quotient and gradient, and the convergence property is similar to classical gradient-based algorithm. Finally, the numerical experiments on benchmark functions show the excellent performance of the two algorithms and the fast convergence speed of DCSO.

Key words: Clouds search optimization algorithm, Intelligent optimization,Function optimization, Difference quotient in- formation, Gradient