Blindman-walking Optimization Method

Optimization methods are all implemented with the hypothesis of unknowing the mathematic express of objective objection. Using the human analogy innovative method, the one-dimension blind-walking optimal method is proposed in this paper. The theory and the algorithm of this method includes halving, doubling, reversing probing step and verifying the applicability condition. Double-step is available to make current point moving to the extremum point. Half-step is available to accelerate convergence. In order to improve the optimization, the applicability condition decides whether update current point or not. The operation process, algorithmic flow chart and characteristic analysis of the method were given. Two optimization problems with unimodal or multimodal objective function were solved by the proposed method respectively. The simulation result shows that the proposed method is better than the ordinary method. The proposed method has the merit of rapid convergence, little calculation capacity, wide applicable range, etc. Taking the method as innovative kernel, the random research method, feasible direction method and complex shape method were improved. Taking the innovative content of this paper as innovative kernel, a monograph was published. The other innovations of the monograph are listed, such as applied algorithm of Karush-Kuhn-Tucker (KKT) conditions on judging the restriction extremum point, the design step of computing software, the complementarity and derivation of Powell criterion, the method of keeping the complex shape not to deduce dimension and the analysis of gradual optimization characteristic, the reinforced wall of inner point punish function method, the analysis of problem with constrained monstrosity extremum point, the improvement of Newton method and the validation of optimization idea of blind walking repeatedly, the explanation of later-day optimization method, the conformity of seeking algorithm needing the objective function derivation, e- - tc.