Development of the discrete orca predation algorithm

The Traveling Salesman Problem (TSP) is one of the most frequently studied combinatorial optimization problems today. It is used to solve many real-global problems such as scheduling, circuit design, layout design of facilities in factories, route planning and printed circuit design. Therefore, researchers in the field of discrete optimization methods consider it as a realistic testbed and evaluate the performance of new algorithms on it. In this study, a discrete version of the Orca Predation Algorithm (OPA), called the Discrete Orca Predation Algorithm (DOPA), is developed and used to solve the TSP. Like OPA, DOPA consists of two phases: chase and attack. In the chase phase, the distances between orcas are calculated using Hamming distance and the velocity values are obtained using these calculated values. The locations of the orcas are updated using the velocity values and the 2 opt algorithm. In the attack phase, the positions of the orcas are calculated with the Order Crossover (OX1) operator. In the position adjustment procedure, the swap local search operator is used. The parameters of the DOPA are tuned by the Taguchi statistical method. DOPA is tested on 67 well-known TSP samples. Furthermore, Friedman and Wilcoxon signed rank tests are applied to check whether there are significant differences between DOPA and the other nine current methods. The experimental results have shown that DOPA is an alternative and highly competitive method to the other methods.