Cooperative control of multiple mobile robot using particle swarm optimization for tracking two passive target
This paper proposes a cooperative control method for multiple mobile robots to track two passive targets in unknown environment on the basis of a particle swarm optimization (PSO). By adjusting the left and right wheels speed of the mobile robot using the proposed control method, each mobile robot can move towards the target on the basis of information about the previous best location of each particle and its neighbors. The simulation results demonstrate the validity of the proposed cooperative control method of multiple mobile robots for tracking two passive targets.
Cooperative mobile robots with obstacle and collision avoidance using fuzzy logic
This paper presents a fuzzy controller technique in navigation with obstacle and collision avoidance for cooperative micro robots playing soccer. The dynamic positions of the robots and obstacles are taken into consideration. The information needed about the robot environment is the destination point, the distance between the robots, and obstacles and is taken from a camera vision system. The obstacles can be the opponent robots, the teammate robots, and the soccer field boundaries. Fuzzy logic is used to control the navigation of a robot as it avoids the presence of an obstacle along its way to the destination point. Obstacle and collision avoidance can be achieved by changing the direction angle of the robot. The cooperative behavior of the robots is tested based on team performance in playing the soccer game. The effectiveness of the technique developed is shown in the results of real time experiments. The fuzzy logic controller developed proves to be more accurate and robust compared to other conventional controllers.
Adaptive global optimization with local search
This dissertation examines the performance of genetic algorithm (GA) hybrids that use local search to solve global optimization problems. GAs are a class of adaptive global sampling methods that take many cues from mechanisms observed in natural evolution. GAs maintain a population of solutions that are used to generate new solutions in the search space. GA hybrids using local search (GA-LS hybrids) are motivated by the apparent need to employ both a global and local search strategy to provide an effective global optimization method. Previous experimental results have found that GA-LS hybrids not only find better solutions than the GA, but also optimize more efficiently. 
To improve the efficiency of GA-LS hybrids, I propose and experimentally validate methods that selectively apply local search to solutions in the GA's population. First, local search is randomly applied with a fixed frequency. Experiments with this method illustrate a trade-off between the refinement performed by local search and the reliability of the competitive search performed by the GA. Next, I describe two classes of adaptive methods. Distribution-based adaptive methods use redundancy in the population to avoid performing unnecessary local searches. Fitness-based adaptive methods use the fitness information in the population to bias the local search towards individuals that have better fitness. An experimental analysis indicates that these adaptive methods can significantly improve the efficiency of GA-LS hybrids. 
This dissertation explores implications of these results for parallel GAs. In particular, a MIMD design for geographically structured genetic algorithms (GSGAs) is described. GSGAs were initially developed for SIMD architectures, where it is difficult to selectively apply local search. An analytic and experimental analysis of MIMD GSGAs demonstrates that they scale well for large problems. 
GA-LS hybrids are used to solve global optimization problems in several application domains. First, GA-LS hybrids are used to find the weights of a neural network to solve the six-bit symmetry problem. Next, they are used to solve a simple 19 atom molecular conformation problem. Finally, they are applied to a drug docking problem. When compared to simulated annealing, GA-LS hybrids not only find better solutions and optimize more efficiently.