Research Of Biogeography-Based Optimization Algorithm And Its Application In Path Planning

Biogeography-Based optimization (BBO) is a new type algorithm inspired by biogeography. The unique mechanism injects new ideas to the field of optimization algorithm research, and many research achievements about biogeography also provide profound theoretical foundation for the algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. Individual migration operator is designed based on the probability, in order to achieve information sharing among individuals. This article describes the basic knowledge of biogeography. In BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as emigration and immigration. This paper describes the equilibrium species count in biogeography theory, describes the behavior of six different migration models in BBO. In addition, the design principle, process, migration and mutation operators are detailed. The paper also discusses the difference of the BBO and the tradition optimization algorithm. Through several test functions, BBO is compared with ant colony optimization, genetic algorithms, Differential Evolution, evolutionary strategy, Population-Based Incremental Learning and StudGA. The experiments show that BBO is an algorithm that has much promise and merits further development and investigation.BBO has shown its ability to solve optimization problems. However, in order to improve this advantage relative to other heuristic algorithms, it is necessary to improve BBO. The second goal of this paper is to improve the performance of BBO by adding some features from other algorithms. The feature is borrowed from DE, and the new method is called differential biogeography-based optimization. Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we propose a hybrid algorithm of BBO and DE, namely BBODE, for the global numerical optimization problem. To verify the performance of our proposed BBODE,19 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experiment results indicate that our approach is effective and efficient. BBODE is superior to BBO, while it can compete with DE in the term of accuracy of solution and the convergence of process.Path planning is one of the most critical technologies among all of the robotics researches. Biogeography-based optimization is a new algorithm, it is not yet applied to the path planning. Researching the path planning of mobile robot with biogeography-based optimization method is the last task of this dissertation. Global path planning of mobile robot in a static environment is the most important problem. This paper discussed the feasibility about a modified method of global path planning based on a hybrid biogeography-based optimization with differential evolution in a static environment. The method defined the pots (the number of SIV on islands) according to the number of the obstacles in order to widely applied to different conditions. Experimental results in simulation prove that the method is correct and feasible.