Improved Estimation Of Distributed Algorithm And Its Application In Optimization Design

Optimization design has become an independent discipline,and gradually penetrated in various industries.The traditional optimization algorithm is used in the early development of optimization design.With the development of swarm intelligence evolutionary algorithm,more and more swarm intelligence algorithms are applied in the optimization design.Estimation of distributed algorithm is known as a swarm evolutionary algorithm which is based on probability model and has strong global searching ability.The evolution of the algorithm is too dependent on the solution space,makes evolution process is slow.The innovation of this paper is to improve the MIMIC algorithm.,put forward two algorithms with effective calculation,feasible solution and high-precision,and then we apply it into two simple examples of optimization design.The results show that the algorithm proposed in this paper is important in practical application.Particularly,it provides a new idea and method for solving the optimization problem.The main contributions of this paper as follows:In the evolution process of MIMIC algorithm,adding a pattern search method with strong local search ability.A hybrid MIMIC algorithm that combined with pattern search method is proposed.To Increase the diversity of the population,first,we will selects a number of points in the current population as the initial point for pattern search,and then translate it as a part of the new population.The effective of algorithm for the six test functions is measured by the following three performance indicators: optimal evolution within a fixed value,algebraic evolution,compliance rate to verify the feasibility and validity of the method,the improved algorithm refinement.And through the comparison of MIMIC algorithm and the improved algorithm in different dimensions after the results obtained,the higher the dimension,the MIMIC algorithm and improved MIMIC algorithm optimization ability is low,the effect of convergence ability of the complexity function of the algorithm,but the higherthe dimension,the improved MIMIC algorithm is more obvious advantages.By combining MIMIC algorithm with Rosenbrock algorithm in the evolution,we proposed a hybrid MIMIC algorithm combined with Rosenbrock algorithm.To solve the shortcomings of population evolution later smaller individual differences,we will select some dominant group in the MIMIC algorithm,and then randomly select some point in current population,and save as the starting point of the rotation direction search method.The new point will be a part of the new group.The results show that the improved algorithm combines the MIMIC algorithm global search ability with the Rosenbrock algorithm of local refinement ability.Besides,the algorithm does not require the objective function is differentiable.So,it is an effective algorithm for solving the objective functions which are not differentiable or derivative.The proposed algorithm which combined MIMIC algorithm with Rosenbrock algorithm has been applied to the worm model.It is useful to find the appropriate value of worm head,modulus and diameter coefficient and making the smallest worm gear ring volume.The optimization results show that the improved algorithm of optimal value and evolution are smaller than the standard MIMIC algorithm.Rounding the results,and then compare with the conventional design optimization,the volume decreasing 31%,this shows that the improved algorithm is feasible in worm model.The hybrid MIMIC algorithm have been applied to the welding beam model,which is a minimization of the total cost problem.By comparing with the standard MIMIC algorithm and other two algorithms,we find that the results of improved algorithm in significantly less than other algorithms,which demonstrate that the improved the algorithm is effective in welding beam model.