33 research outputs found

    Hypercube FrameWork for ACO applied to timetabling

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    We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

    Hypercube FrameWork for ACO applied to timetabling

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    We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

    New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

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    Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

    Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

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    U radu se daje prijedlog dizajna paradigme algoritma za optimizaciju kolonije mrava primjenom Hyper-Cube sustava za rješenje problema programiranja računarskog projekta (Software Project Scheduling Problem). Taj se NP-hard problem sastoji od davanja zaduženja zaposlenicima u svrhu smanjenja trajanja projekta i njegovih ukupnih troškova. To zaduženje mora zadovoljiti ograničenja problema i pitanje prvenstva među zadacima. Pristup prikazan ovdje koristi Hyper-Cube sustav za uspostavljanje eksplicitno multidimenzionalnog prostora za kontrolu ponašanja mravi. Time nam se omogućava autonomno vođenje istraživanja u cilju pronalaženja ohrabrujućih rješenja.This 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

    Using a Parallel Ensemble of Sequence-Based Selection Hyper-Heuristics for Electric Bus Scheduling

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    This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordA Sequence-based Selection Hyper-Heuristic (SSHH) utilises a hidden Markov model (HMM) to generate sequences of low-level heuristics to apply to a given problem. The HMM represents learnt probabilistic relationships in transitioning from one heuristic to the next for generating good sequences. However, a single HMM will only represent one learnt behaviour pattern which may not be ideal. Furthermore, using a single HMM to generate sequences is sequential in manner but most processors are parallel in nature. Consequently, this paper proposes that the effectiveness and speed of SSHH can be improved by using multiple SSHH, an ensemble. These will be able to operate in parallel exploiting multi-core processor resources facilitating faster optimisation. Two methods of parallel ensemble SSHH are investigated, sharing the best found solution amongst SSHH instantiations or combining HMM information between SSHH models. The effectiveness of the methods are assessed using a real-world electric bus scheduling optimisation problem. Sharing best found solutions between ensembles of SSHH models that have differing sequence behaviours significantly improved upon sequential SSHH results with much lower run-times.Innovate UKCity Scienc

    Optimization for Decision Making II

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    In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

    An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning

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    A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases

    Evolutionary Algorithms with Mixed Strategy

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    Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation.

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    Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs. The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations. The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention
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