Discrete Sine-Cosine Algorithm (DSCA) with Local Search for Solving Traveling Salesman Problem

Abstract

One of the new population-based optimization algorithms, named sine-cosine algorithm (SCA), is introduced to solve continuous optimization problems. SCA utilizes the sine and cosine functions to recast a set of potential solutions to balance between exploration and exploitation in the search space. Many researchers have developed and introduced a modified version of SCA to solve engineering problems, multi-objective version of SCA to solve multi-objective engineering design problems, and a binary version of SCA to deal with datasets. Our goal from this work to propose discrete SCA (DSCA) to solve the traveling salesman problem (TSP). The TSP is one of the typical NP-hard problems. DSCA works on the basic concepts of exploration and exploitation. To balance the exploration and exploitation in DSCA, it uses two different mathematical expressions to update the solutions in each generation. DSCA is combined with 2-opt local search method to improve exploitation. To enhance the exploration heuristic crossover, it is united with the proposed DSCA. A benchmarks problem selected from TSPLIB is used to test the algorithm, and the results show that the DSCA algorithm proposed in this article is comparable with the other state-of-the-art algorithms over a wide range of TSP.