Dynamic path planning using a modified genetic algorithm

Genetic algorithm (GA) is well-known algorithm to find a feasible path planning which can be defined as global optimum problem. The drawback of GA is the high computation due to random process on each operator. In this research, the new initial population integrating with new crossover operator strategy was proposed. The parameter is the length of distance travelled of the robot. Before employing the crossover operator, generating a c-obstacle have been done. The c-obstacle is used as a filter to reduce unnecessary nodes to decrease time computation. After that, the initial population has been determined. The initial population is divided into two parents which parent’s chromosome contains an initial and goal position. The second parents are fulfilled with nodes from each obstacle. The genes of chromosome will add with c-obstacle nodes. Crossover operator is applied after filtering and c-obstacle of possible hopping is determined. Filtering method is used to remove unnecessary nodes that are part of c-obstacle. Fitness function considers the distance from the last to next position. Optimum value is the shortest distance of path planning which avoids the obstacle in front. The aim of the proposed method is to reduce the random population and random operating in GA. By using a similar data set of previous researches, the modified GA can reduce the total of generation and yield an adaptive generation number. This means that the modified GA converges faster than the other GA methods.