Discrete Social Spider Algorithm for the Traveling Salesman Problem

Abstract

Heuristic algorithms are often used to find solutions to real complex world problems. These algorithms can provide solutions close to the global optimum at an acceptable time for optimization problems. Social Spider Algorithm (SSA) is one of the newly proposed heuristic algorithms and based on the behavior of the spider. Firstly it has been proposed to solve the continuous optimization problems. In this paper, SSA is rearranged to solve discrete optimization problems. Discrete Social Spider Algorithm (DSSA) is developed by adding explorer spiders and novice spiders in discrete search space. Thus, DSSA's exploration and exploitation capabilities are increased. The performance of the proposed DSSA is investigated on traveling salesman benchmark problems. The Traveling Salesman Problem (TSP) is one of the standard test problems used in the performance analysis of discrete optimization algorithms. DSSA has been tested on a low, middle, and large-scale thirty-eight TSP benchmark datasets. Also, DSSA is compared to eighteen well-known algorithms in the literature. Experimental results show that the performance of proposed DSSA is especially good for low and middle-scale TSP datasets. DSSA can be used as an alternative discrete algorithm for discrete optimization tasks.