# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 2 Ant Colony Optimization
### Recent Trends in ACO Algorithms  
Among ACO scientific literature, three main research lines can be distinguished. On the one hand, the majority of the state-of-the-art works are clearly focused on the application of ACO metaheuristics to different and computationally challenging applications and domains, such as UAV search missions [19], maximizing influence in social networks [20] or in wireless sensor networks [21]. For more ACO applications, the reader is kindly referred to [17].  
On the other hand, there is a challenging research line (in which this work is also included) that aims to improve ACO metaheuristics through the inclusion of modifications or mechanisms that aim to diversify the search to promising regions of the search space while keeping a good balance between diversification/intensification. Within this research line, we can find several hybrid algorithm proposals that combine ACO with other techniques. For instance, the Hybrid Ant Colony Optimization Algorithm (HACO, [22]) introduces an operator similar to the mutation operator of the genetic algorithms. Or the Best-Worst Ant System (BWAS) [13], integrates components inspired by evolutionary computation such as the inclusion of mutations of the pheromone table. The papers in [23; 24] consider the hybridization of ACO with genetic algorithms to solve the supplier selection and multiple sequence alignment problems, respectively. Additionally, the authors of [25] consider an annealing ACO with a mutation operator to solve the TSP. Furthermore, BWAS is hybridized with Particle Swarm Optimization (PSO) ideas in [26] and tested over for TSP benchmark. Another example of hybridization of ACO and PSO is given in [27], related to PID parameter optimization on autonomous underwater vehicle control systems. Other approaches include the hybridization of ACO algorithms with differential evolution in [28; 29] to solve TSP and cancer data classification problems. Additionally, the ACO metaheuristic proposed in [30] presents a different approach to diversify the solution space based on combining pairs of searching ants. Lastly, another interesting research line is the study of parameter adaptation techniques, that is, the variation of ACO parameter settings while solving an instance of an optimization problem [31].  
Finally, a third current line of research concerns the application of the ACO algorithms to dynamic discrete problems [32]. Some particular features of the ACO algorithms, such as the pheromone-based memory, have proven to be remarkably successful for static combinatorial optimization problems, but such features do not seem to be so favorable for dynamic problems, where some parts of the problem can change at runtime without previous knowledge [33]. In such cases, if the ACO algorithm has achieved convergence to a confined region of the search space which becomes irrelevant after the change in the problem, the algorithm can find it difficult to efficiently escape from that region and adapt its memory structure to the new problem change. As a way to overcome such a limitation, a method called P-ACO has been proposed to optimize the reuse of pheromone information after the problem changes [34]. Other alternative methods are based on the use of immigrant schemes for dynamic problems [35; 36]. A very recent paper considers the role of parameter setting and the use of enhanced hybridization with local search procedures [37]. The results in [37] show that the hybridization of MMAS (one of the best ACO performers for static problems) with local search can consistently outperform P-ACO for dynamic combinatorial optimization problems.