Many optimization methods and practical softwares have been developing for many years and most of them are very effective, especially to solve practical problems. But, non-linearity of objective functions and constraint functions, which have frequently seen in practical problems, has caused a difficulty in optimization. This difficulty mainly lies in the existence of several local optimum solutions.
In this study, we have proposed a new global optimization methodology that provides an information exchange mechanism in the nearest neighbor method. We have developed a simple software system, which treated each design point in optimization as an agent. Many agents can search the optima simultaneously exchanging the their information. We have defined two roles of the agents. Local search agents have roles on searching local optima by such an existing method as the steepest decent method and so on. Stochastic search agents investigate the design space by making use of the information from other agents. Through simple and several structural optimization problems, we have confirmed the advantages of the method.