Parallel Machine Scheduling using Improved Antlion Optimization Algorithm
AbstractAntLion Optimization (ALO) algorithm is one recent of the meta-heuristic algorithms that was developed by Mirjalili in 2015. ALO algorithm imitates the antlion's hunting behaviour in its larvae phase. The long run time of ALO algorithm is the biggest disadvantage of this algorithm. To overcome this deficiency, we proposed some improvements on the mechanisms of the original ALO algorithm. In order to improve the ALO algorithm, firstly, the random walking distance was changed as twenty percent of maximum iteration instead of the maximum iteration number in the original ALO algorithm. We defined new movements between boundaries around the antlion on the phase of trapping antlion pits. In addition, the boundary checking process, the catching prey and rebuilding the pit were developed. The parallel machine scheduling problem (PMS) is defined that it is a set of independent jobs to be scheduled on a number of parallel machines. Scheduling process optimizes the production job sequences in terms of the different patterns. When there are the similar type of machines to be existing in multiple numbers, the jobs can be scheduled over these parallel machines at the same time. To show the performance of improved ALO (IALO) algorithm, some of well-known meta-heuristic algorithms were used in comparison works. The obtained PMS results show that the proposed IALO algorithm has very competitive results in terms of the mean, best, worst cost and standard deviation metrics.