Spider Monkey Optimization to Solve Traveling Salesman Problem

Abstract

Traveling Salesman Problem (TSP) is the most well-known combinatorial optimization real-world problem. TSP is also very popular to check proficiency in any newly developed optimization method. In addition, the optimization methods, which are developed for other tasks (e. g., numerical optimization), also test their proficiency in TSP. This study investigates a new technique to solve TSP based on a recently developed optimization technique stimulated through the foraging conduct of spider monkeys. Standard Spider Monkey Optimization (SMO) is established for numerical optimization which has six phases and each one has a different purpose. In this study, SMO is modified and updated to solve TSP; and Swap Operators (SOs), and Swap Sequence (SS) are considered to adapt SMO for TSP. In the proposed method, each spider monkey is considered as a TSP solution and SS is considered to update the solution. SS is an arrangement of several SOs in which each one holds two particular positions of a solution that might be swapped to make a new solution. All SOs of a SS is applied on a specific tour maintaining order and thus ramifications of the SS change the TSP tour into another one. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or randomly selected spider monkey) of the group. The proposed strategy has been examined on a huge number of benchmark TSPs and final consequences are compared to other prominent methods. SMO shows 11 best result out of 15 benchmark TSP problem compare to Ant Colony Optimization (ACO) and Velocity Tentative PSO (VTPSO). Experimental consequences show that the proposed strategy is a decent technique to solve TSP.