The Study Of Biogeography-based Optimization Algorithm And Its Application

Biogeography-based Optimization (BBO) is a new intelligent optimization algorithm, which was developed by the simulation of the migration of biological organisms on the basis of an overall analysis of the activities in colonial organisms. More and more researchers have focused on BBO due to its unique search mechanism and good optimization performance. BBO is a new research spotlight in the field of intelligent optimization algorithms. However, it is still in its infancy. BBO is used mainly in solving continuous optimization so far. Relatively few results on the application of BBO for discrete optimization are available.According to the characteristics of discrete optimization, a variant of BBO is proposed, which is based on the basic principles of BBO. The variant of BBO can be applied to solve discrete optimization. The main research works and results are as follows in this thesis:1. The problem of emergency facility location based on the linear 0-1 programming is studied. A detailed description of the implementation of the algorithm is given, which is based on the foundational theories of BBO. The experimental results show that the proposed algorithm is efficient and feasible2. The proposed algorithm is extended to solve the nonlinear 0-1 programming and the detailed process of the algorithm is presented. The tests and comparison with other algorithms show that the results obtained from the proposed algorithm is very satisfactory.3. The travelling salesman problem is analyzed, which is one of the classic combinatorial optimization problems. Since the solution of the travelling salesman problem is arrangement of the natural numbers, the coding method based on random keys is designed and the calculating method is given. Experimental results on the travelling salesman problem show the superiority of the proposed algorithm.The research results of this thesis develop and extend the applications of biogeography-based optimization to discrete optimization from its original application in continuous optimization. The proposed algorithm will be a new view of angle for the analysis of discrete problems, and also provides a new competitive method for solving discrete optimization.