Parallel ant system applied to the multiple knapsack problem

Interesting real world combinatorial problems are NP-complete and many of them are hard to solve by using traditional methods. However, several heuristic methods have been developed in order to obtain timely suboptimal solutions. Most of those heuristic methods are also naturally suitable for a parallel implementation and consequently, an additional improvement on the response time can be obtained. One way of increasing the computational power is by using multiple processors operating together on a single problem. The overall problem is split into parts, each of which is operated by a separate processor in parallel. Unfortunately problems cannot be divided perfectly into separate parts and interaction is necessary between the parts like data transfer and process synchronization. However, substantial improvement can be achieved, depending on the problem and the amount of parallelism in the problem. Our work aims to exploit the capability of a distributed computing environment by using PVM and implementing a parallel version of an Ant System for solving the Multiple Knapsack Problem (MKP). An Ant System (a distributed algorithm) is a set of agents working independently and cooperating sporadically in a common problem solving activity. Regarding the above characteristics, an Ant System can be naturally considered as a nearly embarrassingly parallel computation. The proposed parallel implementations of an Ant System are based on two different approaches, static and dynamic task assignment. The computational study involves processors of different velocities and several MKP test cases of different sizes and difficulties (tight and loose constraints). The performance on the response time is measured by two indexes, Speedup Factor and Efficiency when is compared to a serial version of an Ant System. The results obtained show the potential power of exploiting the parallelism underlying in an Ant System regarding the good quality of the results and a remarkable decreasing on the computation time.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Parallel ant system applied to the multiple knapsack problem

Interesting real world combinatorial problems are NP-complete and many of them are hard to solve by using traditional methods. However, several heuristic methods have been developed in order to obtain timely suboptimal solutions. Most of those heuristic methods are also naturally suitable for a parallel implementation and consequently, an additional improvement on the response time can be obtained. One way of increasing the computational power is by using multiple processors operating together on a single problem. The overall problem is split into parts, each of which is operated by a separate processor in parallel. Unfortunately problems cannot be divided perfectly into separate parts and interaction is necessary between the parts like data transfer and process synchronization. However, substantial improvement can be achieved, depending on the problem and the amount of parallelism in the problem. Our work aims to exploit the capability of a distributed computing environment by using PVM and implementing a parallel version of an Ant System for solving the Multiple Knapsack Problem (MKP). An Ant System (a distributed algorithm) is a set of agents working independently and cooperating sporadically in a common problem solving activity. Regarding the above characteristics, an Ant System can be naturally considered as a nearly embarrassingly parallel computation. The proposed parallel implementations of an Ant System are based on two different approaches, static and dynamic task assignment. The computational study involves processors of different velocities and several MKP test cases of different sizes and difficulties (tight and loose constraints). The performance on the response time is measured by two indexes, Speedup Factor and Efficiency when is compared to a serial version of an Ant System. The results obtained show the potential power of exploiting the parallelism underlying in an Ant System regarding the good quality of the results and a remarkable decreasing on the computation time.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony (AC) model for the Multiple Knapsack Problem (MKP). The ant colony metaphor, as well as other evolutionary metaphors, was applied successfully to diverse heavily constrained problems. An AC system is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an AC system is adapted to the MKP. We present some results regarding its performance against known optimum for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Facultad de Informátic

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony (AC) model for the Multiple Knapsack Problem (MKP). The ant colony metaphor, as well as other evolutionary metaphors, was applied successfully to diverse heavily constrained problems. An AC system is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an AC system is adapted to the MKP. We present some results regarding its performance against known optimum for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Balance de carga adaptativo basado en un sistema híbrido neuro-difuso para sistemas distribuidos de uso intensivo de CPU

La performance de un sistema distribuido puede ser mejorada si se utilizan estaciones de trabajo ociosas o con poca carga, para la ejecución de tareas que son asignadas inicialmente a estaciones de trabajo que no tienen capacidad suficiente. En este trabajo se presenta una estrategia para balance de carga en sistemas distribuidos para uso intensivo de CPU, basada en un sistema híbrido neuro-difuso. Se proveen además resultados experimentales obtenidos al evaluar la estrategia en un sistema distribuido simulado.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Balance de carga adaptativo basado en un sistema híbrido neuro-difuso para sistemas distribuidos de uso intensivo de CPU

La performance de un sistema distribuido puede ser mejorada si se utilizan estaciones de trabajo ociosas o con poca carga, para la ejecución de tareas que son asignadas inicialmente a estaciones de trabajo que no tienen capacidad suficiente. En este trabajo se presenta una estrategia para balance de carga en sistemas distribuidos para uso intensivo de CPU, basada en un sistema híbrido neuro-difuso. Se proveen además resultados experimentales obtenidos al evaluar la estrategia en un sistema distribuido simulado.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Facultad de Informátic

Un sistema híbrido neuro-genético para la construcción de controladores difusos

En este trabajo se presenta un modelo para la generación de controladores difusos basado en una combinación de redes neuronales y algoritmos genéticos. Un modelo de algoritmo genético denominado evolución simbiótica permite la construcción de las reglas difusas y la generación de los conjuntos difusos utilizados como consecuentes en las reglas. Luego un modelo de red neuronal es utilizado para el ajuste de las reglas difusas obtenidas. El modelo fue validado empíricamente con un problema clásico en el ámbito de sistemas de control: el péndulo invertido.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Un sistema híbrido neuro-genético para la construcción de controladores difusos

En este trabajo se presenta un modelo para la generación de controladores difusos basado en una combinación de redes neuronales y algoritmos genéticos. Un modelo de algoritmo genético denominado evolución simbiótica permite la construcción de las reglas difusas y la generación de los conjuntos difusos utilizados como consecuentes en las reglas. Luego un modelo de red neuronal es utilizado para el ajuste de las reglas difusas obtenidas. El modelo fue validado empíricamente con un problema clásico en el ámbito de sistemas de control: el péndulo invertido.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI