An Optimized Scalable Multi-ant Colony System for Multi-depot Vehicle Routing Problems Using a Reactive Multi-agent System

The multi-depot vehicle routing problem is a variant of the vehicle routing problem that tries to minimize the total cost of providing the service from several depots to satisfy several client demands. This paper presents a multi-ant colony system to solve the multi-depot vehicle routing problem using a reactive agent-based approach. This approach is designed to effectively solve the problem, in which each reactive agent is inspired by modeling the behavior of the ant. We define two types of reactive agents whose behavior differs in the use of two kinds of pheromone trail. In order to refer to the two phases of the execution process, i.e., the assignment phase and the routing phase, every reactive agent cooperates with others to provide a scalable solution for the overall problem. The solution of the multi-depot vehicle routing problem is beneficial and helpful for many real applications. The performance evaluation of the proposed approach is done using instances from the literature, and the results obtained demonstrate good performance when compared with other approaches

Hard limits to cognitive flexibility: ants can learn to ignore but not avoid pheromone trails

Learning allows animals to respond to changes in their environment within their lifespan. However, many responses to the environment are innate, and need not be learned. Depending on the level of cognitive flexibility an animal shows, such responses can either be modified by learning or not. Many ants deposit pheromone trails to resources, and innately follow such trails. Here, we investigated cognitive flexibility in the ant Lasius niger by asking whether ants can overcome their innate tendency and learn to avoid conspecific pheromone trails when these predict a negative stimulus. Ants were allowed to repeatedly visit a Y-maze, one arm of which was marked with a strong but realistic pheromone trail and led to a punishment (electroshock and/or quinine solution), and the other arm of which was unmarked and led to a 1 M sucrose reward. After circa 10 trials ants stopped relying on the pheromone trail, but even after 25 exposures they failed to improve beyond chance levels. However, the ants did not choose randomly: rather, most ants begun to favour just one side of the Y-maze, a strategy which resulted in more efficient food retrieval over time, when compared to the first visits. Even when trained in a go/no-go paradigm which precludes side bias development, ants failed to learn to avoid a pheromone trail. These results show rapid learning flexibility towards an innate social signal, but also demonstrate a rarely seen hard limit to this flexibility.

The multi-dimensional nature of information drives prioritization of private over social information in ants

When personally gathered and socially acquired information conflict, animals often prioritize private information. We propose that this is because private information often contains details that social information lacks. We test this idea in an ant model. Ants using a food source learn its location and quality rapidly (private information), whereas pheromone trails (social information) provide good directional information, but lack reliable information about food quality. If this lack is indeed responsible for the choice of memory over pheromone trails, adding information that better food is available should cause foragers to switch their priority to social information. We show it does: while ants follow memory rather than pheromones when they conflict, adding unambiguous information about a better potential food source (a 2 µl droplet of good food) reverses this pattern, from 60% of ants following their memory to 75% following the pheromone trail. Using fluorescence microscopy, we demonstrate that food (and thus information) flows from fed workers to outgoing foragers, explaining the frequent contacts of ants on trails. Ants trained to poor food that contact nest-mates fed with good food are more likely to follow a trail than ants which received information about poor food. We conclude that social information may often be ignored because it lacks certain crucial dimensions, suggesting that information content is crucial for understanding how and when animals prioritize social and private information.

A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem

Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality.

Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control

A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.

Same length, different shapes: ants collectively choose a straight foraging path over a bent one

In socials insects, exploration is fundamental for the discovery of food resources and determines decision-making. We investigated how the interplay between the physical characteristics of the paths leading to food sources and the way it impacts the behaviour of individual ants affects their collective decisions. Colonies of different sizes of Lasius niger had access to two equal food sources through two paths of equal length but of different geometries: one was straight between the nest and the food source, and the other involved an abrupt change of direction at the midway point (135°). Both food sources were discovered simultaneously, but the food source at the end of the straight path was preferentially exploited by ants. Based on experimental and theoretical results, we show that a significantly shorter duration of nestbound travel on the straight path, which rapidly leads to a stronger pheromone trail, is at the origin of this preference.