Distributed Biogeography Based Optimization for Mobile Robots

I present hardware testing of an evolutionary algorithm (EA) known as distributed biogeography based optimization (DBBO). DBBO is an extended version of biogeography based optimization (BBO). Typically, EAs require a central computer to control the evaluation of candidate solutions to some optimization problem, and to control the sharing of information between those candidate solutions. DBBO, however, does not require a centralized unit to control individuals. Individuals independently run the EA and find a solution to a given optimization problem. Both BBO and DBBO are based on the theory of biogeography, which describes how organisms are distributed geographically in nature. I have compared the performance of BBO and DBBO by using fourteen benchmark functions that are commonly used to evaluate the performance of optimization algorithms. I perform both hardware and simulation experiments. Wallfollowing robots are used as hardware to implement the DBBO algorithm. Robots use two different controllers to maintain a constant distance from the wall: one is a proportional integral derivative (PID) controller and the other is a fuzzy controller. DBBO optimizes the performance of the robots with respect to the control parameters. During simulation experiments I used different EA mutation rates; different staring points for the robots; and different wheel bases. I have also done T-tests to analyze the statistical significance of performance differences and robustness tests to analyze the performance