Narrow Results

In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pedestrians and vehicles as well as the mutual effect between evacuation individuals and the environment which evacuees are situated in, will have an important impact on evacuation effects. This paper presents a pedestrian–vehicle mixed evacuation model to produce optimal evacuation plans considering both evacuation time and density degree. A co-evolutionary multi-particle swarms optimization approach is proposed to simulate the evacuation process of pedestrians and vehicles separately and the interaction between these two kinds of traffic modes. The proposed model and algorithm are effective for mixed evacuation problems. An illustrating example of a study region around a large stadium has been presented. The experimental results indicate the effective performances for evacuation problems which involve complex environments and various types of traffic modes.

As a meta-heuristic algorithm, the ant colony algorithm has been successfully used to solve various combinatorial optimization problems. However, the existing algorithm that takes the power of ants to solve distributed constraint optimization problems (ACO_DCOP) is easy to fall into local optima. To deal with this issue, this paper presents an adaptive ant colony algorithm based on local information entropy to solve distributed constraint optimization problems, named LIEAD. In LIEAD, the local information entropy is introduced to help agents adaptively select the pheromone update strategy and value selection strategy, which improves the convergence speed and the quality of the solution. Moreover, a restart mechanism is designed to break the accumulation state of pheromone, which increases the population diversity and helps the algorithm jump out of the local optima. The extensive experimental results indicate that LIEAD can significantly outperform ACO_DCOP and is competitive with the state-of-the-art DCOPs algorithms.

In this paper, an improved Ant Colony System algorithm applied to image edge detection is presented. During their movement on image, artificial ants establish pheromone graph which represents the image edge information. The ant movement is directed by the local variation of the image’s intensity values. To improve this method, supplementary behaviors are added to each ant in response to its local stimuli, i.e., the ant self-reproduces and directs its progenitors to an appropriate direction to explore more suitable areas. Moreover, it dies if it exceeds a specific iteration age and so the ineffective searches are eliminated. These additional behaviors allow diversifying the exploration performed by ants and also reinforcing the exploitation of these ants’ search experience. Proposed approach allows having more accurate and more complete edges. The performance is tested visually with various images and empirically with evaluation parameters.

Improved ant colony optimization (ACO) algorithms for continuous-domain optimization have been widely applied in recent years, but these improved methods have a weak perception of environmental information changes and only rely on the residues of the pheromones in the path to guide colony evolution. In this paper, we propose an ant colony algorithm based on the reinforcement learning model (RLACO). RLACO can acquire more environmental information by calculating the diversity of the ant colony, and, uses the diversity and other basic information of the ant colony to establish a reinforcement learning model. At different stages of evolution, the algorithm chooses an optimal strategy that can maximize the reward to improve the global search ability and convergence speed of the colony. The experimental results on CEC 2017 test functions show that the proposed algorithm is superior to other algorithms for continuous-domain optimization in convergence speed, accuracy and global search ability.

This paper presents an application of Ant Colony Optimization (ACO) algorithm to tune the parameters in the design of a novel type of nonlinear proportional-integral-differential (NLPID) controller, which is used in flight simulator. A differential tracker and a reference generator are included in the NLPID controller. The differential tracker is used to track output position and its differential, while the reference generator is used to generate reference input signals. ACO algorithm is a novel heuristic algorithm, which is based on the process of real ants in the nature searching for food. In order to tune the parameters of the NLPID controller using the new proposed grid-based ACO algorithm, an objective function based on position tracing error is constructed. The parameters tuning of NLPID can be summed up as the typical continual spatial optimization problem, grid-based searching strategy is adopted in the improved ACO algorithm, and self-adaptive control strategy for the pheromone decay parameter is also adopted. In order to enhance the searching speed, pair ants which searched from two different mesh points at the same time are used. The body structure and control system architecture of a type of flight simulator with the grid-based ACO algorithm optimized NLPID are also proposed. The simulation results demonstrate that both for the standard and random input signals, the tracking error is very small, and the whole control system with grid-based ACO algorithm optimized NLPID has quick response performance and strong robustness.

Boolean modeling has been successfully applied to the budding yeast cell cycle to demonstrate that both its structure and its timing are robustly designed. However, from these studies few conclusions can be drawn how robust the cell cycle arrest upon osmotic stress and pheromone exposure might be. We therefore implement a compact Boolean model of the S. cerevisiae cell cycle including its interfaces with the High Osmolarity Glycerol (HOG) and the pheromone pathways. We show that all initial states of our model robustly converge to a cyclic attractor in the absence of stress inputs whereas pheromone exposure and osmotic stress lead to convergence to singleton states which correspond to G1 and G2 arrest in silico. A comparison with random Boolean networks reveals, that cell cycle arrest under osmotic stress is a highly robust property of the yeast cell cycle. We implemented our model using the novel frontend booleannetGUI to the python software booleannet.

Performance of ants clustering is very sensitive to the key parameters in ants algorithm, this paper analyses how to adjust key parameters adaptively to improve ants clustering. It also brings forward a improved method making use of pheromone and ant’s local memory to direct ant to move effectively, whereas ants are moving randomly after picking up or dropping an object in recent ants clustering algorithm. As the parallel nature of ants cluster, we apply AACA in web mining to mine correlative page set and make it suitable to dispose dynamic and large quantity of web log.