Optimization is a process of choosing the best alternative ways to solve a problem among a different set of options or it can be one of the major quantitative techniques in decision making in which decisions have to be taken to optimize one or more objectives in some prescribed set of constraints. Optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), Ant colony optimization simulated annealing (SA), etc., were used in solving job shop scheduling problem (JSSP). There are different kinds of these algorithms that were used in several previous works. In previous literatures, that the initial solution were generally guessed in a very random manner. In this work, we will deal with the impact of such random initialization on solving the JSSP using PSO. The performance of the algorithm will be evaluated with different set of initial conditions. In this experiment, during initialization stage, the initial swarm will be initialized with random schedules. The impact of the initial condition on the performance of this algorithm has been studied using the convergence curve and the achieved makespan. The arrived results proved that the conventional way of randomly selecting initial conditions of the evolutionary process hasa worst effect on performance in JSSP of higher dimensions. While initializing with known, worst case solution, the evolutionary process was capable of converging into meaningful and more optimum solutions.
Indian Member 40.00
Others Member 3.00