Solving Combinatorial Optimization Problems With Single Seekers Society Algorithm

Abstract

The single seekers society (SSS) algorithm is a recently developed meta-heuristic algorithm for solving complex continuous optimization problems. The aim of this paper is to adapt the SSS algorithm to handle combinatorial optimization problems. As the original SSS algorithm does, the combinatorial SSS algorithm also brings several single-solution based search algorithms together while making them to communicate through an information mechanism based on the superposition principle and reproduction procedure. Therefore, the algorithmic logic remains the same for the combinatorial SSS algorithm; however, some components are modified to suit combinatorial problems. Performance of the combinatorial SSS algorithm is tested on the well-known combinatorial optimization problems such that the vehicle routing problem with simultaneous pickup and delivery, the vehicle routing problem with mixed pickup and delivery, the flow shop scheduling problem, and the job shop scheduling problem. This paper also compares the SSS algorithm against different solution approaches in the related literature on routing and scheduling problems. Experimental results indicate that the SSS algorithm has satisfactory performance and high capability in solving combinatorial optimization problems. © 2020 Elsevier B.V.