A Comparison of Artificial Bee Colony algorithm and Genetic Algorithm to Minimize the Makespan for Job Shop Scheduling

Abstract

Job shop scheduling is predominantly an Non deterministic polynomial (NP)- complete challenge which is successfully tackled by the ABC algorithm by elucidating its convergence. The Job Shop Scheduling Problem (JSSP) is one of the most popular scheduling models existing in practice which is among the hardest combinatorial optimization problems. The ABC (Artificial Bee Colony) technique is concerned, it is observed that the entire specific artificial bees move about in a search space and select food sources by suitably adapting their location, know-how and having a full awareness of their nest inhabitants. Moreover, several scout bees soar and select the food sources discretely without making use of any skills. In the event of the quantity of the nectar in the fresh source becoming larger than the nectar quantity of an available source, they remember the fresh location and conveniently disregard the earlier position. In this way, the ABC system integrates local search techniques, executed by employed and onlooker bees, with universal search approaches, administered by onlookers and scouts. In our ambitious approach we have employed these three bees to furnish optimization in makespan, machine work load and the whole run period in an optimized method. In this way, with the efficient employment of our effective technique we make an earnest effort to minimize the makespan and number of machines. This paper compares GA to minimize the make span of the job scheduling process with ABC and proved that ABC algorithm produces the better result.