The ant colony metaphor in continuous spaces using boundary search

This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Current applications of ant systems for subset problems

Early applications of Ant Colony Optimization (ACO) have been mainly concerned with solving ordering problems (e.g., the Traveling Salesman Problem). In this report we describe an Ant System algorithm, which would be appropriate for solving additional subset problems as was showed for solving the multiple knapsack problem in previous works. The experiments on progress show the potential power of the ACO approach for solving different subset problems.Eje: Sistemas inteligentes. Metaheurísticas.Red de Universidades con Carreras en Informática (RedUNCI

Current applications of ant systems for subset problems

Early applications of Ant Colony Optimization (ACO) have been mainly concerned with solving ordering problems (e.g., the Traveling Salesman Problem). In this report we describe an Ant System algorithm, which would be appropriate for solving additional subset problems as was showed for solving the multiple knapsack problem in previous works. The experiments on progress show the potential power of the ACO approach for solving different subset problems.Eje: Sistemas inteligentes. Metaheurísticas.Red de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor in continuous spaces using boundary search

This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.Eje: Agentes y Sistemas Inteligentes (ASI)Red 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

Achieving an appropriate balance between precision, support, and comprehensibility in the evolution of classification rules

This article proposes a method for achieving an appropriate balance between the parameters of support, precision, and simplicity during the evolution of classification rules by means of genetic programming. The method includes an adaptive procedure in order to achieve such balance. This work lies within the data mining context, more precisely, it focuses on the extraction of comprehensible knowledge where the approach introduced plays a predominant role. Experimental results demonstrate the advantages of using the proposed methodRed de Universidades con Carreras en Informática (RedUNCI

Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

An improved ant colony algorithm for the job shop scheduling problem

Instances of static scheduling problems can be easily represented as graphs where each node represents a particular operation. This property makes the Ant Colony Algorithms well suited for different kinds of scheduling problems. In this paper we present an improved Ant System for solving the Job Shop Scheduling (JSS) Problem. After each cycle the Ant System applies a scheduler builder to each solution. The schedule builder is able to generate under a controlled manner different types of schedules (from non-delay to active). Any improvement achieved for a solution will affect the performance of the algorithm in the next cycles by changing accordingly the amount of pheromone on certain paths. Since the pheromone is the building block of an ant algorithm, it is expected that these changes guide the search towards more promising areas of the search space. The computational study involves a set of instances of different size and difficulty. The results are compared against the best solutions known so far and results reported from earlier studies of ant algorithms applied to the JSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic