A review: On using ACO based hybrid algorithms for path planning of Multi-Mobile Robotics

The path planning for Multi Mobile Robotic (MMR) system is a recent combinatorial optimisation problem. In the last decade, many researches have been published to solve this problem. Most of these researches focused on metaheuristic algorithms. This paper reviews articles on Ant Colony Optimisation (ACO) and its hybrid versions to solve the problem. The original Dorigo’s ACO algorithm uses exploration and exploitation phases to find the shortest route in a combinatorial optimisation problem in general without touching mapping, localisation and perception. Due to the properties of MMR, adaptations have been made to ACO algorithms. In this review paper, a literature survey of the recent studies on upgrading, modifications and applications of the ACO algorithms has been discussed to evaluate the application of the different versions of ACO in the MMR domain. The evaluation considered the quality, speed of convergence, robustness, scalability, flexibility of MMR and obstacle avoidance, static and dynamic environment characteristics of the tasks

Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time