A Method for Planning the Transportation Route of Fresh Agricultural Products Based on Ant Colony Algorithm

The current fresh agricultural product transportation route planning matrix is generally set in a single form, resulting in low path planning efficiency and increased path transportation time. Therefore, the design and analysis of a fresh agricultural product transportation route planning method based on the ant colony algorithm is proposed in this paper. The optimal path problem and planning nodes are described based on actual planning needs and standards. A multi-objective form is adopted to improve path planning efficiency, and a multi-objective optimal planning matrix and ant colony calculation line planning model are designed. Based on this, path planning processing is implemented using time and space route correction. The test results show that compared to the traditional CiteSpace fresh agricultural product transportation route planning test group and the traditional improved genetic algorithm fresh agricultural product transportation route planning test group, a better control of the path transportation time within 150 minutes is achieved by the ant colony algorithm fresh agricultural product transportation route planning test group designed this time. This indicates that the designed transportation route is considered the optimal route. Furthermore, after research, it was found that the arrival times of the six selected delivery points were all before the expected earliest arrival time, suggesting that this planning method has greater effectiveness and practical application value

Ant colony optimization based simulation of 3d automatic hose/pipe routing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis focuses on applying one of the rapidly growing non-deterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, multi-objective hose routing and multi-hose routing. The use of simulation and optimization in engineering design has been widely applied in all fields of engineering as the computational capabilities of computers has increased and improved. As a result of this, the application of non-deterministic optimization techniques such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. has increased dramatically resulting in vast improvements in the design process. Initially, two versions of ant colony algorithms have been developed based on, respectively, a random network and a grid network for a single objective (minimizing the length of the hoses) and avoiding obstacles in the CAD model. While applying ant colony algorithms for the simulation of hose routing, two modifications have been proposed for reducing the size of the search space and avoiding the stagnation problem. Hose routing problems often consist of several conflicting or trade-off objectives. In classical approaches, in many cases, multiple objectives are aggregated into one single objective function and optimization is then treated as a single-objective optimization problem. In this thesis two versions of ant colony algorithms are presented for multihose routing with two conflicting objectives: minimizing the total length of the hoses and maximizing the total shared length (bundle length). In this case the two objectives are aggregated into a single objective. The current state-of-the-art approach for handling multi-objective design problems is to employ the concept of Pareto optimality. Within this thesis a new Pareto-based general purpose ant colony algorithm (PSACO) is proposed and applied to a multi-objective hose routing problem that consists of the following objectives: total length of the hoses between the start and the end locations, number of bends, and angles of bends. The proposed method is capable of handling any number of objectives and uses a single pheromone matrix for all the objectives. The domination concept is used for updating the pheromone matrix. Among the currently available multi-objective ant colony optimization (MOACO) algorithms, P-ACO generates very good solutions in the central part of the Pareto front and hence the proposed algorithm is compared with P-ACO. A new term is added to the random proportional rule of both of the algorithms (PSACO and P-ACO) to attract ants towards edges that make angles close to the pre-specified angles of bends. A refinement algorithm is also suggested for searching an acceptable solution after the completion of searching the entire search space. For all of the simulations, the STL format (tessellated format) for the obstacles is used in the algorithm instead of the original shapes of the obstacles. This STL format is passed to the C++ library RAPID for collision detection. As a result of using this format, the algorithms can handle freeform obstacles and the algorithms are not restricted to a particular software package

Anti-pheromone as a tool for better exploration of search space

Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their communication, ant algorithms have typically focused on the use of just one, a substance that encourages succeeding generations of (artificial) ants to follow the same path as previous generations. Ant algorithms for multi-objective optimisation and those employing multiple colonies have made use of more than one pheromone, but the interactions between these different pheromones are largely simple extensions of single criterion, single colony ant algorithms. This paper investigates an alternative form of interaction between normal pheromone and anti-pheromone. Three variations of Ant Colony System that apply the anti-pheromone concept in different ways are described and tested against benchmark travelling salesman problems. The results indicate that the use of anti-pheromone can lead to improved performance. However, if anti-pheromone is allowed too great an influence on ants' decisions, poorer performance may result

Routing design for less-than-truckload motor carriers using ant colony techniques

One of the most important challenges for Less-Than-Truck-Load carriers consists of determining how to consolidate flows of small shipments to minimize costs while maintaining a certain level of service. For any origin-destination pair, there are several strategies to consolidate flows, but the most usual ones are: peddling/collecting routes and shipping through one or more break-bulk terminals. Therefore, the target is determining a route for each origin-destination pair that minimizes the total transportation and handling cost guaranteeing a certain level of service. Exact resolution is not viable for real size problems due to the excessive computational time required. This research studies different aspects of the problem and provides a metaheuristic algorithm (based on Ant Colonies Optimization techniques) capable of solving real problems in a reasonable computational time. The viability of the approach has been proved by means of the application of the algorithm to a real Spanish case, obtaining encouraging results

QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017