Search Results (7)

Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that is being solved. By using pheromones, the algorithm builds a probabilistic model that is exploited for constructing new and, hopefully, better solutions. Traditionally, there are two different strategies for updating pheromone trails. The best-so-far strategy (global best) is rather greedy and can cause a too-fast convergence of the algorithm toward some suboptimal solutions. The other strategy is named iteration best and it promotes exploration and slower convergence, which is sometimes too slow and lacks focus. To allow better adaptability of ant colony optimization algorithms we use κ-best, max-κ-best, and 1/λ-best strategies that form the entire spectrum of strategies between best-so-far and iteration best and go beyond. Selecting a suitable strategy depends on the type of problem, parameters, heuristic information, and conditions in which the ACO is used. In this research, we use two representative combinatorial NP-hard problems, the symmetric traveling salesman problem (TSP) and the asymmetric traveling salesman problem (ATSP), for which very effective heuristic information is widely known, to empirically analyze the influence of strategies on the algorithmic performance. The experiments are carried out on 45 TSP and 47 ATSP instances by using the MAX-MIN ant system variant of ACO with and without local optimizations, with each problem instance repeated 101 times for 24 different pheromone reinforcement strategies. The results show that, by using adjustable pheromone reinforcement strategies, the MMAS outperformed in a large majority of cases the MMAS with classical strategies. Full article

The popularity of unmanned aerial vehicle (UAV) technology has made UAV logistics transportation possible. However, based on the current development status of logistics UAVs, there are difficulties in using UAVs directly for door-to-door logistics transportation. Therefore, it is necessary to establish UAV parcel-receiving stations that can gather logistics needs in a small area. The construction of stations allows the UAVs to transport back and forth between the distribution warehouse and the established stations, enabling customers to send and receive packages at the more convenient stations. Based on the current situation, it is a more appropriate air–ground cooperative transport mode to solve the “last-mile” cargo transportation problem. In this paper, a bi-layer UAV parcel-receiving station and public air-route planning method is proposed to explore the interaction between station location and public route planning, and is solved with a genetic algorithm and max–min ant system (GA-MMAS). The model proposed in this paper can determine the location of the stations and plan the public air routes between the warehouse and stations simultaneously. Simulation results show that the planning results of the bi-layer optimization model proposed in this paper meet the requirements of station location and public air-route planning. Compared with the layered planning results, the cost of the upper-layer model is reduced by 5.12% on average, and the cost of the lower-layer model is reduced by 4.48%. Full article

Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark. Full article

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning. Full article

Agricultural machinery rental is a new service form that uses big data in agriculture to improve the utilization rate of agricultural machinery and promote the development of the agricultural economy. To realize agricultural machinery scheduling optimization in cloud services, a dynamic artificial bee-ant colony algorithm (DABAA) is proposed to solve the above problem. First, to improve the practicability of the mathematical model in agricultural production, a dynamic coefficient is proposed. Then the mutation operation is combined with the artificial bee colony (ABC) algorithm to improve the algorithm. Then, iterative threshold adjustment and optimal fusion point evaluation are used to combine the ABC algorithm with the ant colony optimization (ACO) algorithm, which not only improves the search precision but also improves the running speed. Finally, two groups of comparison experiments are carried out, and the results show that the DABAA can obviously improve the running speed and accuracy of cloud services in agricultural machinery rental. Full article

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems. Full article

Parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In this paper, the MAX-MIN Ant System algorithm (MMAS) is parallelized to solve Traveling Salesman Problem (TSP) based on a Spark cloud computing platform. We combine MMAS with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer cluster. To improve the precision of the solution, local optimization strategy 2-opt is adapted in MMAS. The experimental results show that Spark has a very great accelerating effect on the ant colony algorithm when the city scale of TSP or the number of ants is relatively large. Full article