The Learnable Ant Colony Optimization (LACO) is proposed to satellite ground station system scheduling problems. The LACO employs an integrated modelling idea which combines the ant colony model with the knowledge model. In order to improve the performance, LACO largely pursues the complementary advantages of ant colony model and knowledge model. Experimental results suggest that LACO is a feas...

BACKGROUND Deep brain stimulation of the anterior nucleus of the thalamus (ANT) is an emerging therapy for refractory focal epilepsy. However, the most optimal target for stimulation has not been unambiguously described. OBJECTIVE In the present study, we investigated the correlation between the stimulation site and outcome in order to define the optimal target for deep brain stimulation in r...

This paper conducts experimental tests to study the stagnation behavior the Interacted Multiple Ant Colonies Optimization (IMACO) framework. The idea of different ant colonies use different types of problem dependent heuristics has been proposed as well. The performance of IMACO was demonstrated by comparing it with the Ant Colony System (ACS) the best performing ant algorithm. The computationa...

It is well documented that ants can protect plants against insect herbivores, but the underlying mechanisms remain almost undocumented. We propose and test the pheromone avoidance hypothesis--an indirect mechanism where insect herbivores are repelled not only by ants but also by ant pheromones. Herbivores subjected to ant predation will experience a selective advantage if they evolve mechanisms...

Path planning is crucial in the scheduling and motion of multiple robots. However, solving multi-robot path-planning problems efficiently quickly challenging due to their high complexity long computational time, especially when dealing with many This paper presents a unified mathematical model algorithm for path robots moving from one formation another an area obstacles. The problem was initial...

Effective rural governance is the foundation for achieving revitalization and promoting modernization of China’s system capacity in new era. The elucidation influencing factors driving pathways underlying effective has significant importance facilitating advancement revitalization. Drawing upon Actor-Network Theory (ANT), this study introduces an analytical framework “human actor dimension—non-...

A component-based approach to model Ant Colony System (ACS) for the Traveling Salesman Problem (TSP) is shown in this paper. The used components to solve the TSP with ACS technique and the collaboration rules between components are also described. The internal reasoning about the ant colony component-based system for the TSP gives a better perception of solving this problem using ant-based tech...

The ant system is a new meta-heuristic for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. It was rst proposed for tackling the well known Traveling Salesman Problem (TSP), but has been also successfully applied to problems such as quadratic assignment, job-shop scheduling, vehicle routing and grap...

The Ant System is a new meta heuristic method particularly appropriate to solve hard combinatorial optimization problems It is a population based nature inspired approach exploiting positive feedback as well as local information and has been applied successfully to a variety of combinatorial optimization problems The Ant System consists of a set of cooperating agents arti cial ants and a set of...

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect comm...