Conventional Ant Colony Algorithm Research Articles

3D PathPlanning of RobotFishBased on ImprovedAntColonyAlgorithm

For the following problems:three-dimensional obstacle avoidance path selection when using robot fish to find targets in a complex coral reef geographical environment, an improved ant colony algorithm that can be applied in an environment with multiple complexities is proposed. Combined with the underwater terrain, several maps with different obstacle complexities are established with the three-dimensional environment modeling scheme. Aiming at easy stagnation, slow search speed, and easiness to produce local optimization in the conventional ant colony algorithm, this paper improved the function design and helped the robot fish carry out the early search based on three pieces of heuristic information (the global distance, traffic ability cost, and path consumption cost). Then the path is sorted according to the path length to adjust the pheromone update mode. At the same time, an elite ant system is constructed to adjust the pheromone value of elite ants separately. The simulation results show that convergence speed and searchabilityof the algorithm have been improved to some extent, which proves feasibility of the algorithm.

A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles

Recently, the ship energy consumption issue has been in the spotlight. Meanwhile, new conventions and standards of ship energy conservation as well as ship emission reduction have been continuously introduced by the International Maritime Organization and governments around the world. Energy conservation is a crucial factor in ship route planning. This paper proposes an improved ant colony algorithm that can handle a complicated ocean current environment and find an optimal energy-saving path. Firstly, this paper focuses on both the path length and energy consumption. To reduce energy consumption, a comprehensive energy cost function is introduced by a conventional ant colony algorithm. In addition, this paper analyzes the influence of the ocean current speed and setting on a marine vehicle and establishes an improved ant colony algorithm model based on the influence of the ocean current. Finally, simulation tests are carried out in Lianyungang and its surrounding areas to test the energy consumption performance of the improved ant colony algorithm. The simulation result shows that the improved ant colony algorithm can significantly reduce the energy consumption by 3.326% compared with the traditional algorithm.

Spectral line extraction based on ant-colony algorithm

A spectral-line-extraction algorithm based on the ant-colony algorithm is proposed to address the difficulty of extracting spectral lines in low signal-to-noise ratio conditions, and the problem that results from the optimal path algorithm falls into local optimization. The algorithm applies the ant-colony path-optimization strategy to detect a spectral line and constructs a corresponding mathematical model using the grid method. A new cost function is proposed to replace path length as the optimization standard in the conventional ant-colony algorithm. At the same time, the roulette rule is used to determine the direction of the next step. This algorithm improves the traditional heuristic function, increases the attraction of the target spectral line to the route search, and improves the convergence rate. Sea-trial data show that the algorithm performs better in extracting spectral lines with a low signal-to-noise ratio than the optimal path algorithm.

Research on path planning of cleaning robot based on an improved ant colony algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

Traffic Forecast Based on Statistical Data for Public Transport Optimization in Real Time

The intellectualization of transportation system is a relevant task for solving traffic-related problems such as vehicle traffic management and modern transport system improvement. The purpose of this work was to design a competitive pathway for real transport system to optimize the route planning. Modeling of the transport system refers to the problem of finding the K-shortest sustainable pathway in a multimodal network. The solution to this problem was fulfilled by applying a hybrid algorithm of an ant colony based on a differential evolution approach to the pheromone renewal and the division of the colony into teams. Experimental results showed that an advanced ants colony algorithm is highly efficient even with quite a small population of the colony. The obtained results were compared with those of the conventional ant colony algorithm. Due to its high efficiency, the elaborated method is applicable in improving the quality of the entire road network, especially in congestions and traffic jams, taking into account real-time traffic information.

U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem

This paper provides a new Ant based algorithms called U-Turning Ant colony optimization (U-TACO) for solving a well-known NP-Hard problem, which is widely used in computer science field called Traveling Salesman Problem (TSP). Generally U-Turning Ant colony Optimization Algorithm makes a partial tour as an initial state for the basic conventional Ant Colony algorithm. This paper provides tables and charts for the results obtained by U-Turning Ant colony Optimization for various TSP problems from the TSPLIB95.

U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem

This paper provides a new Ant based algorithms called U-Turning Ant colony optimization (U-TACO) for solving a well-known NP-Hard problem, which is widely used in computer science field called Traveling Salesman Problem (TSP). Generally U-Turning Ant colony Optimization Algorithm makes a partial tour as an initial state for the basic conventional Ant Colony algorithm. This paper provides tables and charts for the results obtained by U-Turning Ant colony Optimization for various TSP problems from the TSPLIB95.

Tool path length optimisation of contour parallel milling based on modified ant colony optimisation

Reducing machining time in a milling process is one of the important criteria to improve the overall efficiency of the machining process. This paper presents a study on the reduction of machining time, focusing on contour parallel machining to increase the efficiency and performance during the machining process. One method to enhance the performance of contour parallel machining is by defining a tool path interval that is larger than the radius of the cutting tool in a roughing operation because of its capability of reducing the tool path length and machining time. However, this causes the occurrence of an uncut region at the corner and at the centre of a contour parallel. This uncut region can be removed through an additional tool path known as the clear tool path. Therefore, in this paper, a new method based on an optimisation technique is introduced to generate a clear tool path that removes the entire uncut region in contour parallel machining at minimum cutting time. Ant colony algorithm (ACO) is used to optimise the clear tool path length in contour parallel machining time by minimising the movement of cutting tool in removing the entire uncut regions. A new transition rule has been established from the conventional ACO, which adapted the uncut region occurring at the corner of the contour parallel. Then, to validate the optimisation result, a cutting experiment was carried out using computer numerical control (CNC) milling machine. It can be ascertained from this study that the optimisation of the clear tool path gives optimal tool path length whilst reducing the cutting time in the roughing process.

Solving route optimisation problem in logistics distribution through an improved ant colony optimisation algorithm

In this paper, aiming at conventional Ant Colony algorithm's defects and shortcomings, we introduce Genetic Algorithm to improve it. By the GA's reproduction, crossover and mutation operators, the ACA's convergence rate and global searching ability have a significant improvement. Besides, we improve the updating mode of pheromone to enhance the adaptability of ants, the ACA can automatic adjust pheromone residual degree when executing the algorithm for convergence. Besides, introducing a new deterministic searching method will accelerate the heuristic searching method rate. After the description of our improved algorithm, we do two groups of experiments, the results show that our proposed algorithm has a good effect on solving logistics distribution routing optimisation problem, compared with the conventional algorithm, our experiments are on large logistics distribution route sets, the results show that our improved algorithm can get the optimal solution rapidly and accurately, the results are more robust than conventional results.

The Application of an Improved Ant Colony Algorithm in Mobile Robot Path Planning

This paper presents an improved ant colony algorithm to plan an optimal collision-free path for mobile robot in complicated static environment. Based on the work space model with grid method, simulated foraging behavior of ants and to serve the mobile robot path planning, update the conventional ant colony algorithm with some special functions. To avoid mobile robot path deadlock, a dead-corner table is established and the penalty function is used to update the trail intensity when an ant explores a dead—corner in the path searching. The simulation results show that the algorithm can improve performance of path planning obviously, and the algorithm is simple and effective.

An optimized ant colony algorithm based on the gradual changing orientation factor for multi-constraint QoS routing

The ad hoc network has been attracting increasing attention of researchers owing to its good performance and special application. The search of route that satisfies such multi-constraints as delay, jitter and bandwidth in ad hoc network can facilitate the solution to multi-media transmission. The problem of multi-constraint is generally an NP-Hard problem. This paper deals with the problem by adding an orientation heuristic factor to the conventional ant colony algorithm, which enables the ant to get rid of the blindness at the initial stage of path searching. The ant in the modified algorithm not only makes use of the previous search findings, but also reduces the misguiding effect of pheromones on the irrelevant paths, thus overcoming the problem of slow convergence. The choice and the extent of the effect of the orientation heuristic factor in the modified algorithm is our focus, and the gradual changing orientation factors is also studied. The gradual changing orientation factor not only enables the ant to take advantage of direction to assist path search, but also adjust convergence speed and exactitude. Simulation results indicate that the modified algorithm can quickly find the feasible solution to the network routing problem. The method adopted can help find better solutions in shorter time.

Multicast routing for delay variation bound using a modified ant colony algorithm

The delay and delay variation-bounded Steiner tree problem is an important multicast routing problem in real-time multimedia networks. Such a constrained Steiner tree problem is known to be NP-complete. This paper proposes an ant colony algorithm with orientation factor and applies it to multicast routing problem with the constraints of delay variation bound. The orientation factor enables the ant to get rid of the initial blindness when searching paths, makes use of the search results and reduces the misguiding effect of pheromone on irrelevant paths, thus overcoming the drawbacks of slow convergence existing in the basic ant colony algorithm, increasing the speed of convergence and speeding up the finding of feasible solution to the problem. The simulation results show that the modified algorithm makes it possible to find a feasible solution to the multicast routing problem with delay variation bound. Compared with the conventional ant colony algorithm, the convergence speed of the modified algorithm is improved.