Discrete orca predation algorithm for the traveling salesman problem

The traveling salesman problem (TSP) is a frequently studied problem by researchers today and belongs to the class of combinatorial optimization problems. It can be used to solve many current world problems such as scheduling, circuit design, layout design of plants in factories, route planning and printed circuit design. Therefore, researchers working in the field of optimization methods use it as a realistic test environment and evaluate the performance of new algorithms on it. In this study, a discrete version of the orca predation algorithm (OPA) was developed, called DOPA. DOPA is a novel discrete and permutation-coded optimization algorithm. As in OPA, it consists of two phases: chasing and attacking. However, these phases in DOPA were arranged to be able to solve a combinatorial optimization problem such as TSP. In the chasing phase, the distances between the orcas were calculated with the Hamming distance, and the speed values were obtained by using these distances. The positions of the orcas were updated using the speed values and 2-opt algorithm. In the attacking phase, the positions of the orcas were calculated by order crossover (OX1) operator. In the position adjustment procedure, swap local search operator was used. Thus, the convergence of the algorithm was accelerated. DOPA has much less parameters than OPA. Therefore, it is a simple and effective algorithm with few parameters. The parameters of DOPA were optimized by the Taguchi statistical method. It was tested on 67 well-known TSP instances. The results of different performance measures and tests showed that DOPA is a highly competitive and alternative method compared to other methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.