Ant Colony Optimization Using Common Social Information and Self-Memory

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to ?nd a solution for the traveling salesman problem (TSP). The rank-based ant system (AS(rank)) has been proposed as a developed version of the fundamental model AS of ACO. In the AS(rank), since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the AS(rank) can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the AS(rank), also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP

Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory

Here, we develop a new cellular automata-based traffic model. In this model, individual vehicles cannot estimate global traffic flows but can only detect the vehicle ahead. Each vehicle occasionally adjusts its velocity based on the distance to the vehicle in front. Our model generates reversible phase transitions in the vehicle flux over a wide range of vehicle densities, and the traffic system undergoes scale-free evolution with respect to the flux. We thus believe that our model reveals the relationship between the macro-level flows and micro-level mechanisms of multi-agent systems for handling traffic congestion, and illustrates how drivers’ decisions impact free and congested flows. Document type: Articl

Localization of Heat Shock Protein 27 (Hsp27) in the Rat Gingiva and its Changes with Tooth Eruption

Heat shock protein 27 kDa (Hsp27) functions as a molecular chaperon to prevent apoptosis as well as to contribute to the regulation of cell proliferation and differentiation during development. In the present study, the localization of Hsp27 in the oral epithelium of rats and its expression change during formation of the gingiva with the tooth eruption were examined immunohistochemically to elucidate the roles of Hsp27 in the oral mucosa

Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem

In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions

The Müller-Lyer illusion in ant foraging.

The Müller-Lyer illusion is a classical geometric illusion in which the apparent (perceived) length of a line depends on whether the line terminates in an arrow tail or arrowhead. This effect may be caused by economic compensation for the gap between the physical stimulus and visual fields. Here, we show that the Müller-Lyer illusion can also be produced by the foraging patterns of garden ants (Lasius niger) and that the pattern obtained can be explained by a simple, asynchronously updated foraging ant model. Our results suggest that the geometric illusion may be a byproduct of the foraging process, in which local interactions underlying efficient exploitation can also give rise to global exploration, and that visual information processing in human could implement similar modulation between local efficient processing and widespread computation