Multiobjective multicast routing with Ant Colony Optimization

This work presents a multiobjective algorithm for multicast traffic engineering. The proposed algorithm is a new version of MultiObjective Ant Colony System (MOACS), based on Ant Colony Optimization (ACO). The proposed MOACS simultaneously optimizes the maximum link utilization, the cost of the multicast tree, the averages delay and the maximum endtoend delay. In this way, a set of optimal solutions, known as Pareto set is calculated in only one run of the algorithm, without a priori restrictions. Experimental results obtained with the proposed MOACS were compared to a recently published Multiobjective Multicast Algorithm (MMA), showing a promising performance advantage for multicast traffic engineering.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed

Indicator Based Ant Colony Optimization for Multi-objective Knapsack Problem

AbstractThe use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many algorithms have been proposed in the literature to solve different MOP. This paper presents an indicator-based ant colony optimization algorithm called IBACO for the multi-objective knapsack problem (MOKP). The IBACO algorithm proposes a new idea that uses binary quality indicators to guide the search of artificial ants. These indicators were initially used by Zitzler and Künzli in the selection process of their evolutionary algorithm IBEA. In this paper, we use the indicator optimization principle to reinforce the best solutions by rewarding pheromone trails. We carry out a set of experiments on MOKP benchmark instances by applying the two binary indicators: epsilon indicator and hypervolume indicator. The comparison of the proposed algorithm with IBEA, ACO and other state-of-the-art evolutionary algorithms shows that IBACO is significantly better on most instances

Dispatching Requests for Agent-Based Online Vehicle Routing Problems with Time Windows

Vehicle routing problems are highly complex problems. The proposals to solve them traditionally concern the optimization of conventional criteria, such as the number of mobilized vehicles and the total costs. However, in online vehicle routing problems, the optimization of the response time to the connected travelers is at least as important as the optimization of the classical criteria. Multi-agent systems on the one hand and greedy insertion heuristics on the other are among the most promising approaches to this end. In this paper, we propose a multi-agent system coupled with a regret insertion heuristic. We focus on the real-time dispatching of the travelers\u27 requests to the vehicles and its efficiency. A dispatching protocol determines which agents perform the computation to answer the travelers\u27 requests. We evaluate three dispatching protocols: centralized, decentralized and hybrid. We compare them experimentally based on their response time to online travelers. Two computational types are implemented: a sequential implementation and a distributed implementation. The results show the superiority of the centralized dispatching protocol in the sequential implementation (32.80% improvement in average compared to the distributed dispatching protocol) and the superiority of the hybrid dispatching protocol in the distributed implementation (59.66% improvement in average, compared with the centralized dispatching protocol)

Optimización basada en colonias de hormigas: una aplicación a la distribución de sobres

El presente trabajo propone un método de distribución de sobres basado en el conocido problema de la literatura, el MTSP - Multiple Traveling Salesman Problem. Se propone una solución para una empresa Paraguaya distribuidora de extractos bancarios. Se consideran 4 objetivos principales: (1) minimizar la cantidad total de vehículos, (2) minimizar la distancia total del recorrido, (3) minimizar el tiempo total de la entrega y (4) maximizar la ganancia total. El trabajo propone una solución basada en ACO – Ant Colony Optimization con enfoque multi-objetivo abordando los 4 objetivos simultáneamente. Resultados Experimentales demuestran que el algoritmo propuesto resuelve eficientemente el problema logístico de distribución de sobres.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimización basada en colonias de hormigas: una aplicación a la distribución de sobres

El presente trabajo propone un método de distribución de sobres basado en el conocido problema de la literatura, el MTSP - Multiple Traveling Salesman Problem. Se propone una solución para una empresa Paraguaya distribuidora de extractos bancarios. Se consideran 4 objetivos principales: (1) minimizar la cantidad total de vehículos, (2) minimizar la distancia total del recorrido, (3) minimizar el tiempo total de la entrega y (4) maximizar la ganancia total. El trabajo propone una solución basada en ACO – Ant Colony Optimization con enfoque multi-objetivo abordando los 4 objetivos simultáneamente. Resultados Experimentales demuestran que el algoritmo propuesto resuelve eficientemente el problema logístico de distribución de sobres.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimización basada en colonias de hormigas: una aplicación a la distribución de sobres

El presente trabajo propone un método de distribución de sobres basado en el conocido problema de la literatura, el MTSP - Multiple Traveling Salesman Problem. Se propone una solución para una empresa Paraguaya distribuidora de extractos bancarios. Se consideran 4 objetivos principales: (1) minimizar la cantidad total de vehículos, (2) minimizar la distancia total del recorrido, (3) minimizar el tiempo total de la entrega y (4) maximizar la ganancia total. El trabajo propone una solución basada en ACO – Ant Colony Optimization con enfoque multi-objetivo abordando los 4 objetivos simultáneamente. Resultados Experimentales demuestran que el algoritmo propuesto resuelve eficientemente el problema logístico de distribución de sobres.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

AntRS: Recommending Lists through a Multi-Objective Ant Colony System

International audienceWhen people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms