A NOVEL EVOLUTIONARY ALGORITHMS BASED ON NUMBER THEORETIC NET FOR NONLINEAR OPTIMIZATION

Abstract: How to detect global optimums which reside on complex function is an important problem in diverse scientific fields. Deterministic optimization strategies, such as the Newton–family algorithms, have been widely applied for the detection of global optimums. However, in the case of discontinuous /non-differentiable /non-convex complex functions, this approach is not valid. In such cases, stochastic optimization strategies simulating evolution process have proved to be a valuable tool. In this paper, a novel evolutionary algorithm based on Number Theoretic Net for detecting global optimums of Complex functions is introduced. It can be applied to any functions with multiple local optimums. With some established techniques, such as the ideas of genetic algorithms and sequential number theoretic optimization to improve the property of convergence in large scale, the deflection and stretching of objective function to guarantee the detection of a different minimizer, it detects optimums of a function through adding genetic operations to the feasible points generated by number theoretic net sequentially. The experiments done indicate that the proposed algorithm is robust and efficient