## 2 Related Work  
The research community has shown great interest in solving tangible, real world problems via methods applicable to CO problems. Recently, many authors have been actively trying to enhance conventional optimization methods by introducing principles and techniques originating from unconventional methods of computation in the hope that they would prove superior to traditional approaches. For example, [PERSON] et al. [7] proposed several novel techniques which they called quantum-inspired ant colony optimization, quantum-inspired differential evolution, and quantum-inspired particle swarm optimization, respectively, for multi-level color image thresholding. These techniques find optimal threshold values at different levels of thresholding for color images. A new quantum-inspired social evolution (QSE) algorithm was proposed by hybridizing a well-known social evolution algorithm with an emerging quantum-inspired evolutionary one. The proposed QSE algorithm was applied to the 0-1 knapsack problem and the performance of the algorithm was compared to various evolutionary, swarm, and quantum-inspired evolutionary algorithmic variants. [PERSON] and [PERSON] claim that the performance of the QSE algorithm is better than or at least comparable to the different evolutionary algorithmic variants it was tested against [8]. [PERSON] et al. proposed a decentralized form of quantum-inspired particle swarm optimization with a cellular structured population for maintaining population diversity and balancing global and local search [9]. [PERSON] et al. conducted an interesting study by applying a novel hybrid quantum-inspired evolutionary algorithm to a permutation flow-shop scheduling problem. They proposed a simple representation method for the determination of job sequence in the permutation flow-shop scheduling problem based on the probability amplitude of qubits [10]. [PERSON] et al. designed a quantum-inspired space search algorithm in order to solve numerical optimization problems. In their algorithm, the feasible solution is decomposed into regions in terms of quantum representation. The search progresses from one generation to the next, while the quantum bits evolve gradually to increase the probability of region selection [11]. [PERSON] et al. in [12] proposed a novel approach using a quantum-inspired algorithm based on game-theoretic principles. In particular, they reduced the problem they studied to choosing strategies in evolutionary games. Quantum games and their strategies seem very promising, offering enhanced capabilities over classic ones [13]. Solutions based on variable neighborhood search (VNS) have been applied to route planning problems. [PERSON] et al. proposed a hybrid adaptive variable neighborhood search algorithm for solving the capacitated vehicle routing problem (VRP) [14]. A two-level VNS heuristic has been developed in order to tackle the clustered VRP by [PERSON] and [PERSON] [15]. In [16], a VNS approach for the solution of the recently introduced swap-body VRP is proposed. [PERSON] et al. made an extensive comparative study of well known methods and ready-to-use software and they concluded that no software or classic method can guarantee an optimal solution to the TSP problems that model GIS problem with more than 25 nodes [17]. [PERSON] et al. proposed a GVNS approach for the TSP with time windows [4] and a quantum-inspired GVNS (qGVNS) for solving the TSP with time windows [18]. Moreover, the proposed method can also be applied on other classes of problems from real world, such as optimization on localization in hospitals, smart cities, and smart parking systems. [PERSON] et al. applied RFID technologies to tag-to-tag communication paradigms in order to achieve improved energy-efficiency and operational effectiveness [19]. [PERSON] et al. presented a system for trip planning that consolidates future traffic threats [20]. Specifically, this system measures traffic flow in areas with low sensor coverage by using a Gaussian Process Regression.