An Efficient Evolutionary Metaheuristic for the Traveling Repairman (Minimum Latency) Problem

A Guided Quantum Particle Swarm Optimization Approach for the Traveling Repairman Problem

“…Table 3 extends Table 4 presented in [22]. It provides an overview of the outcomes achieved by our proposed approach, as well as the three aforementioned methods.…”

“…To evaluate the efficacy of our GQPSO approach, we compared their results for small and large instances with those reported by the proposed algorithms in the literature such as the memetic algorithm (MA) proposed in [18], the GRASP+VND/VNS algorithm proposed in [19], the GILS-RVND algorithm proposed in [20] and the DBMEA algorithm proposed in [22]. The comparison between the different methods was conducted in terms of solution quality and execution time.…”

“…In order to conduct a comprehensive evaluation of our GQPSO technique, we compare its outcomes on the aforementioned with those of the top-performing algorithms suggested in the literature for tackling the TRP, including the GILS-RVND algorithm proposed in [20] and the DBMEA algorithm proposed in [22]. These two approaches were all proposed to solve the TRP where the solutions are given as Hamiltonian circuits.…”

“…The method they suggested has only been evaluated on considerably small instances, primarily those involving 10 nodes. To date, the most effective heuristic method discovered in previous studies for the TRP is the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), developed by T-Szab et al [22]. The authors introduced a hybrid approach in their research, which combines Furuhashi's Bacterial Evolutionary Algorithm with various discrete local search techniques.…”

Effect of the initial population construction on the DBMEA algorithm searching for the optimal solution of the traveling salesman problem

“…In this section, a heuristic optimization approach will be briefly introduced which has proven rather efficient for a wide family of TSP related problems (mostly, extensions of the original TSP towards more realisticand more complexcases), while having two additional advantageous properties: the algorithm is generally applicable with high efficiency, and the runtime is rather predictable in terms of the size of the problem (number of nodes in the graph) [27]. Thus, even though for the basic TSP there is a better heuristic known, but it is very tailor made and not applicable for any other related optimization, and so we intend to investigate a complex approach which will be later extendable for other similar NPhard problems.…”