Search

Ant colony optimization (ACO) is a very suitable path search algorithm, whose typical application is traveling salesman problem. However, as one heuristic algorithm, it has many shortcomings, such as slow convergent speed and low searching efficiency. To overcome these shortcomings, the premium-penalty strategy has been introduced, and the pheromone diversity of the good paths and the ordinary ones is increased to polarize pheromone density of all paths. Thus, premium-penalty ant colony optimization (PPACO) is proposed. And its good performance is verified by the applications to some typical traveling salesman problems. Its two important parameters are discussed too. Because location critical slip surface in slope stability analysis is a path search problem, it can be solved by the ACO very suitably. Therefore, based on PPACO and typical mature limit equilibrium analysis (Spencer method), a new method to analyze the slope stability is proposed. Through two typical examples, one simple slope and one complicated slope, the efficiency and effectiveness of the new algorithm are verified. The results show that, the new algorithm can always find the less safety factor and its critical slip surface in shorter time than many previous algorithms, and the new algorithm can be used in real engineering very well. [ABSTRACT FROM AUTHOR]

A path planning method based on improved RRT (Rapidly-Exploring Random Tree) algorithm was proposed in application to local path planning of USV (Unmanned Surface Vehicle). Aiming at the high speed and real-time control of USV, the inhibitory factor, limited angle and distance heuristic information were introduced into classical RRT algorithm, thus the selection of exploration points and growing points were modified, and the speed of the algorithm was improved. The excess navigation points in planning path were processed and smoothed considering the gyration performance to make the navigation distance shorter and meet the special demands of maneuver performance of USV. The experiment of local path planning was completed based on the environment model constructed by the process results of typical radar images in the sea and lake experiments. The experimental results showed that the suggested algorithm could rapidly complete the path search, the efficient of algorithm was improved and the distance of path was reduced, the planning path after optimization treatment could satisfy the planning system need. The suggested method can apply to USV local path planning. [ABSTRACT FROM AUTHOR]

In this paper the authors propose a structural method for a genetic algorithm (GA) for the optimization problem of cable routing in which cables have to be laid optimally. When there are no limits on the layout routes, the optimal routes for the individual cables can be found using the Dijkstra method in a finite discrete graph. However, when there are limits on the capacity in the layout routes, optimization cannot be performed using the Dijkstra method. The proposed GA is a two-level hierarchical GA that uses chromosome coding involving two levels. The routes for each cable and the combinations of cable routes are defined respectively, and layout route selection is optimized overall through genetic operations involving these levels. Block crossover and block mutation are used as genetic operations in the first level. In addition, a method to take advantage of lethal genes, which do not satisfy the constraints and are generated in the second level is used. Computer simulations confirm that the two-level hierarchical GA with these genetic operations functions effectively for the optimization problem of cable routing. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(7): 61–71, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10250 [ABSTRACT FROM AUTHOR]