Research On Hierarchical Cellular Alogorithm

In evolutionary algorithm, each individual in the population represent a potential solutions of the problems to be resolved. Mechanism similar to the selection and evolution in nature is used to guide the search direction of the optimal solution. This kind of solution has been used successfully in solve those engineering problem with high complexity. The common problem exist in Evolutionary algorithm is the balance between exploration and exploitation. Research on improve the efficiency of the algorithm to find the global optimum is nowadays highly on the rise, and that is also the purpose of this study.By adding layered operation, computation speed of Cellular Genetic Algorithm has been improved greatly in Hierarchical Cellular Genetic Algorithm (HCGA).In the evolutionary algorithm, calculation speed is related to the ability of tracking the optimal solution, the escape ability from the local optimal is closely related to the diversity of the population. In Hierarchical Cellular Genetic Algorithm, the move rule of cellular individuals is limited to neighbors and individuals in cellular space are move to the single center. In order to further improve the performance of HCGA, the trace ability of optimal solution and the diversity of the population were studied in this paper.The main works of this paper research on Hierarchical Cellular Algorithm is as follows:1) A Hybrid Algorithm Based on HCGA and PSOThis improved algorithm mixed the two algorithm s together. In particle swarm algorithms, position and movement speed of each particle in population is updated with the current optimal solution continuously. The advantages of PSO can make better result of layered operation in HCGA. The improved algorithm can trace the global optimal solution effectively and so as to improve calculation speed of population. Research on algorithms performance under the different optimization strength and comparison among algorithms with the same type has been done in this paper. The experimental results show that Hybrid Algorithm Based on HCGA and PSO could capture the global optimal solution rapidly in the evolution process. 2) Research on Polycentric Hierarchical Cellular Genetic AlgorithmsIn many case, the increase of the convergence speed is on the cost of the diversity lost in population. With The process of evolution, the species diversity lost gradually and the algorithms fall into the local optimum. Therefore, how to keep the diversity of population is the key problem to ensure algorithm iterative operation. In this paper, a new polycentric Hierarchical Cellular Genetic algorithm on the basis of Hierarchical Cellular Genetic Algorithm and central city theory of western economics is proposed.The characteristic feature of new algorithms is choosing a few individual with high fitness in population as the center city. Individuals around the center city moved towards the center, the optimal solution produced in those central cities. The new algorithm greatly improves population is diversity and the searching efficiency. The numerical simulations show that the improved algorithm is more effective and can avoid premature effectively.