The success of Particle Swarm Optimization (PSO) and Genetic algorithm (GA) as single objective optimizer has motivated researchers to extend the use of this bio- inspired techniques to other areas. One of them is multi-objective optimization. As a part of this review we present a classification of the approaches and identify the main approaches here. We describe useful performance measures and simulation results of conventional Genetic algorithm and PSO. We extend this to multi-objective genetic algorithm and PSO. This means that GA and PSO optimizes path based on two criteria: length and difficult. Another method that has new to this field of research is the Artificial Potential field method. In this method the entire space is supposed to contain a potential field and we calculate the net force that is acted upon the robot to reach its goal
