Papers by Keyword: Pheromone

Abstract: Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.

Abstract: In this paper, the vehicle scheduling model in pooling pallet distribution was established, and the genetic algorithm for the problem was researched. In the algorithm, the genetic algorithm and the idea of ant colony algorithm are integrated in the proposed two crossover operators, and the appropriate values of parameters were determined by experiments.

Abstract: The discrete nature of ant colony algorithm (ACO) and the characteristics of the distributed parallel computation and positive feedback had been made it widely used in discrete space problems, but it limited its application in continuous problems and now studies was relatively few. Articles improved the basic ACO to solve problems in continuous domain. The algorithm improved the way of pheromone to keep, update and advance, and limited it in a Max-Min interval at the same time, avoiding the stagnation and restricted diffusion of the algorithm, enhanced the performance of convergence. Simulation example proves that the improved ACO can quickly find good global solution on the continuum.

Abstract: This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

Abstract: Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

Abstract: - Ant algorithms mimic the behavior of the ants where their important behavior is the ability to find the shortest path between food sources and their nest despite being almost blind. In the algorithms, as ants travel, they deposit a chemical substance called pheromone which; together with visibility values is used to make decisions. This paper investigates the effects of pheromone values on solving a routing problem; the capacitated vehicle routing problem (CVRP) In our approach, in order to produce a generalized approach, we developed an ant-based hyper-heuristic where pheromone and visibility values consider a non-domain specific knowledge. In this paper, we propose to provide all visited heuristics with some amount of pheromone. The distribution of pheromone values will be distributed proportioned to the performance done by the ants. This is to encourage the exploration of new edges that might lead to better solutions. We show that our results are better when compared to two other ant algorithm hyper-heuristics in the literature.

Abstract: Bombyx mori pheromone-Binding Protein 1 (BmPBP1) in male moth antennae is a class of Odorant-Binding Proteins (OBPs), it can bind with the specific sex pheromone from female moth, thus initiates the males behaviors like seeking and mating, etc. It has been found that sex pheromone-binding protein 1 is differentially expressed in the antenna of male and female silkworm moths, however, the molecular mechanism of different PBP1 expression and its role in the information transmission are unclear. In this study, we successfully generated the BmPBP1 polyclonal antibody and used it to detect BmPBP1 expression in the silkworm moth antenna. Thus this work is helpful for further studies on the function of BmPBP1 in the information communication between male and female moths.

Abstract: To overcome the limitation of precocity and stagnation in classical ant colony algorithm, this article presents a Parallel Ant System Based on OpenMP. The ant colony is divided into three children ant colonies according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm. By Open Multi-Processing parallel programming idea, the parallel and cooperating optimization of children ant colonies was obtained. It organically combines local search and global search, makes full use of computing power of multi-core CPU, and improves the efficiency significantly. Contrastive experiments show that the algorithm has a better capability of global optimization than traditional ant colony algorithm.

Abstract: Based on the ant colony algorithm analysis and research, this paper proposed an improved ant colony algorithm. Through updating pheromone and optimal search strategy, then applied to the Traveling Salesman Problem (TSP), effectively improved the searching capability of the algorithm. Finally through the simulation testing and analysis, verified that the improved ant colony algorithm is effective, and has good performance.

Abstract: in order to reduce the congestion in AODV protocol and decrease the time taking to find the route, CA-AODV protocol was applied to a routing selection algorithm with cognitive ability of an ant colony algorithm. The algorithm uses probability routing and diffusion of pheromone improving routing search capabilities, around development send congested nodes, to improve the route algorithm performance, enhance the adaptability of the algorithm. By comparing the improved routing algorithm With the original AODV protocol, the simulation results show that, the routing algorithm reduces the end-to-end delay and network congestion.