An Improved Cloud Particle Optimization Algorithm And Its Application

Intelligent computing method is an effective way to solve the multi-objective optimization problem,of which the particle cloud optimization algorithm is a new computing model of the current field.The model simulated the process of formation of cloud and state change in nature,which is a random search mechanism based on "cloud model".Classic cloud particles optimization algorithm has shown a good performance in single-objective optimization problem.But for the multimodal problem,it is difficult to solve the task to ensure completeness.Thus,we proposed an improved cloud particles optimization algorithm based on enthalpy namely ICPEA.Then base on it we proposed an improved multi-objective particles optimization algorithm namely IMOCPEA for solving multi-objective problem.At the same time,in order to verify the effectiveness of the proposed scheme,we analyzed the model through implementing it into two goals logistics center location problem.The main work of this paper includes the following aspects:First,we designed an improved cloud article optimization algorithm based on enthalpy(ICPEA)to solve the problem of the completeness of the classical cloud particle optimization algorithm.By introducing the concept of enthalpy,and the theory of irregular motion of Brownian motion and random walk model,we improved individual distribution characteristics of traditional algorithm,and ensured the independence of individual in the evolution process.Through the experimental analysis of Schwef test function,we found that ICPEA algorithm is beneficial to the maintenance of population diversity.Then,we designed an improved cloud particle optimization algorithm namely IMOCPEA for multi-objective problem solving.On the one hand,the elite strategy ensures the calculation process to converge to the global minimum point with probability one;On the other hand,external file pruning strategy improves the distribution of the algorithm.In order to verify the feasibility and effectiveness of IMOCPEA,four benchmark functions were used for tests,and it was compared with other classical algorithms.The results show that IMOCPEA has good performance optimization.Finally,from a practical application point of view,we established a two goal logistics center location model and used the IMOCPEA algorithm to solve it.Compared with the experimental results of a single target based on evolutionary strategies,the experimental result shows that the algorithm is feasible and effective in solving multi-objective problems.Through the above analysis and research,we can initially concluded that the improved cloud particles optimization algorithm based on enthalpy can be applied not only to multi-objective optimization problem but also to some of the more complex nonlinear engineering problems.