background : cardiac arrest is a condition in which the heart is completely stopped and is not pumping any blood. although most cardiac arrest cases are reported from homes or hospitals, about 20% occur in public areas. therefore, these areas need to be investigated in terms of cardiac arrest incidence so that places of high incidence can be identi-fied and cardiac rehabilitation defibrillators...

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. Thes...

Ant Colony Optimization (ACO) algorithms construct solutions each time starting from scratch, that is, from an empty solution. Similar to ACO, Iterated Greedy is a constructive stochastic local search (SLS) method. However, differently from ACO, Iterated Greedy starts the solution construction from partial solutions. In this paper we examine the performance of a variation of MAX–MIN Ant System,...

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new ...

Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank...

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The adaptation capabilities of ACO rely on the pheromone evaporation mechanism, where the rate is usually fixed. Pheromone evaporation may eliminate pheromone t...

The computational complexity of ant colony optimization (ACO) is a new and rapidly growing research area. The finite-time dynamics of ACO algorithms is assessed with mathematical rigor using bounds on the (expected) time until an ACO algorithm finds a global optimum. We review previous results in this area and introduce the reader into common analysis methods. These techniques are then applied ...

Increasingly complex urban traffic conditions often challenge the express services with long delivery path and much more time as consumed. In this paper, a Traffic Impact Factor (TIF) is introduced to model the impact of urban traffic conditions on express services. Based on the TIF, an optimization model is constructed to minimize the delivery distance and the time consumed. In the solution , ...

To make robot avoid obstacles in 3D space, the Pheromone of Ant Colony Optimization (ACO) in Fuzzy Control Updating is put forward, the Pheromone Updating value varies with The number of iterations and the path-planning length by each ant . the improved Transition Probability Function is also proposed, which makes more sense for each ant choosing next feasible point .This paper firstly, describ...