Akademik Veri Yönetim Sistemi
JOURNAL OF HEURISTICS, cilt.32, sa.3, 2026 (SCI-Expanded, Scopus)
This study presents a competitive algorithm in which the particle swarm optimization algorithm is combined with a simulated annealing procedure and a Lin-Kernighan-Helsgaun heuristic to provide high-quality solutions to the clustered orienteering problem. In this problem, the customers are grouped into clusters, each of which is associated with a profit, and this profit is collected if and only if all the customers in the cluster are served. A single vehicle is available to visit the customers, and each customer can belong to multiple clusters. The goal is to maximize the total collected profit within a maximum limit on the travel time. This study proposes the first swarm-based algorithm with certain innovations to deal with the clustered orienteering problem, which is an NP-hard problem. The salient features of the proposed algorithm are a 'partial move' strategy with a new equation for calculating the next position of a particle, periodic restarting of particles, and a mutation operator to increase the search diversification. The experimental results show that the proposed algorithm is competitive with the state-of-the-art algorithms from the literature. The proposed algorithm achieved the best result on one of the 924 less challenging benchmark instances and on 18 of the 72 highly challenging instances. Moreover, it consistently outperformed all methods in the literature except for the evolutionary algorithm (EA), with no statistically significant difference observed between EA and the proposed algorithm. Nevertheless, the proposed algorithm outperformed EA on the challenging benchmark instances at a confidence level of 91%. Furthermore, the proposed algorithm sets new records (new lower bounds) for one instance of the well-known benchmark sets and 18 of the large instances.