2 results listed
AntLion 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.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Haydar Kılıç
Uğur Yüzgeç
Quadratic Assignment Problem (QAP) is based the facilities allocation, and it is a difficult combinatorial optimization problem. The objective of this problem is to make total assignment cost minimum while being assigned facilities to locations that are already known. To solve this problem, the different methods are used in the literature. Meta-heuristic algorithms are ones from these methods and in this study, we present a new version of recent antlion optimization algorithm for QAP. AntLion Optimization (ALO) algorithm was developed by Mirjalili in 2015. ALO algorithm is based on the hunting behaviour of the antlion. This algorithm comprises five stages: random walking mechanism, constructing trap, trapping in the antlion’s pit, sliding ants in the pit, catching the prey and reconstructing the pit. Although ALO algorithm is successful in benchmark functions of multi dimensions, it has got some drawbacks. The most notable improvement is the use of tournament method instead of roulette wheel method. In ALO algorithm, the antlion is chosen from the population by roulette wheel method for using in each ant's random walking model. The roulette wheel method is more successful in maximization problems. In the minimization problems, the tournament selection method is more efficient method than the other selection methods Therefore, we used the tournament selection method in this study instead of the roulette wheel method on random walking mechanism. This proposed algorithm has been called the tournament selection based antlion optimization algorithm (TALO). To evaluate the performance of TALO algorithm, we used well-known meta-heuristic algorithms. The results provide the proposed TALO algorithm has the best performance in comparison with those of the other algorithms.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Haydar Kılıç
Uğur Yüzgeç