Browsing by Author "Paredes, Fernando"
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Conference Object Citation Count: 2Autonomous Tuning for Constraint Programming via Artificial Bee Colony Optimization(Springer-verlag Berlin, 2015) Mısra, Sanjay; Crawford, Broderick; Mella, Felipe; Flores, Javier; Galleguillos, Cristian; Misra, Sanjay; Paredes, Fernando; Computer EngineeringConstraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.Article Citation Count: 11CHOICE FUNCTIONS FOR AUTONOMOUS SEARCH IN CONSTRAINT PROGRAMMING: GA VS. PSO(Univ Osijek, Tech Fac, 2013) Mısra, Sanjay; Crawford, Broderick; Misra, Sanjay; Palma, Wenceslao; Monfroy, Eric; Castro, Carlos; Paredes, Fernando; Computer EngineeringThe variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.Conference Object Citation Count: 6Comparing Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms for Solving the Set Covering Problem(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Barraza, Jorge; Lizama, Sebastian; Munoz, Alexis; Paredes, FernandoThe set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley's OR-Library.Article Citation Count: 2CONSTRAINT PROGRAMMING FOR OPTIMAL DESIGN OF ARCHITECTURES FOR WATER DISTRIBUTION TANKS AND RESERVOIRS: A CASE STUDY(Univ Osijek, Tech Fac, 2014) Mısra, Sanjay; Crawford, Broderick; Misra, Sanjay; Monfroy, Eric; Palma, Wenceslao; Castro, Carlos; Paredes, Fernando; Computer EngineeringA water distribution system is an essential component of any urban infrastructure system. Its design is commonly a hard task mainly due to the presence of several complex interrelated parameters. Among others, some parameters to study are the water demand, pressure requirements, topography, location of resources, system reliability, and energy uses. In this paper, we focus on a real case of water distribution system in order to minimize installation costs by satisfying the given system requirements. We solve the problem by using state-of-the-art Constraint Programming techniques combined with Interval Analysis for rigorously handling continuous decision variables. Experimental results demonstrate the feasibility of the proposed approach, where the global optimum is reached in all instances and in reasonable runtime.Article Citation Count: 6SOFTWARE PROJECT SCHEDULING USING THE HYPER-CUBE ANT COLONY OPTIMIZATION ALGORITHM(Univ Osijek, Tech Fac, 2015) Mısra, Sanjay; Soto, Ricardo; Johnson, Franklin; Misra, Sanjay; Paredes, Fernando; Olguin, Eduardo; Computer EngineeringThis paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions.Article Citation Count: 5Solving the Software Project Scheduling Problem Using Intelligent Water Drops(Univ Osijek, Tech Fac, 2018) Mısra, Sanjay; Soto, Ricardo; Astorga, Gino; Castro, Carlos; Paredes, Fernando; Misra, Sanjay; Rubio, Jose-Miguel; Computer EngineeringWithin the category of project scheduling problems, there is a specific problem within the software industry referred to as the software project scheduling problem. The problem consists in the correct allocation of employees to the different tasks that make up a software project, bearing in mind time and cost restraints. To achieve this goal, the present work first uses metaheuristic intelligent water drops illustrating; this is a recent stochastic swarm-based method increasingly used for solving optimization problems. Finally, the results and comparisons with experiments performed with other techniques are presented, demonstrating the solidity of the approach presented.