Search results for "Metaheuristic"
showing 10 items of 153 documents
Path relinking and GRG for artificial neural networks
2006
Artificial neural networks (ANN) have been widely used for both classification and prediction. This paper is focused on the prediction problem in which an unknown function is approximated. ANNs can be viewed as models of real systems, built by tuning parameters known as weights. In training the net, the problem is to find the weights that optimize its performance (i.e., to minimize the error over the training set). Although the most popular method for training these networks is back propagation, other optimization methods such as tabu search or scatter search have been successfully applied to solve this problem. In this paper we propose a path relinking implementation to solve the neural ne…
An ILS-Based Metaheuristic for the Stacker Crane Problem
2012
[EN] In this paper we propose a metaheuristic algorithm for the Stacker Crane Problem. This is an NP-hard arc routing problem whose name derives from the practical problem of operating a crane. Here we present a formulation and a lower bound for this problem and propose a metaheuristic algorithm based on the combination of a Multi-start and an Iterated Local Search procedures. Computational results on a large set of instances are presented.
Due Dates and RCPSP
2006
Due dates are an essential feature of real projects, but little effort has been made in studying the RCPSP with due dates in the activities. This paper tries to bridge this gap by studying two problems: the TardinessRCPSP, in which the objective is total tardiness minimization and the DeadlineRCPSP, in which the due dates are strict (deadlines) and the objective is makespan minimization. The first problem is NP-hard and the second is much harder, since finding a feasible solution is already NP-hard. This paper has three objectives: Firstly to compare the performance on both problems of well-known RCPSP heuristics - priority rules, sampling procedures and metaheuristics - with new versions w…
Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms
2019
In this paper, we try to find the most efficient optimization algorithm that can be used to resolve the hydropower optimization problem. We propose a novel optimization technique is called the Split-window method. The method is relatively simple and reduces the complexity of the optimization problem by split-ting the planning horizon (and datasets) into equal windows and assigning the same values to policies(actions) within each part. After splitting, a meta-heuristic technique is used to optimize the actions, and the dataset is split again until a split contains only one instance (timestep). The unique values to be optimized during each iteration is equal to the number of splits which make…
Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management
2013
This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.
Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs
2002
Abstract One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS&M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS&M-related parameters in a plant are associ…
Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography
2009
In this article, various metaheuristics for a numerical optimization problem with application to Electric Impedance Tomography are tested and compared. The experimental setup is composed of a real valued Genetic Algorithm, the Differential Evolution, a self adaptive Differential Evolution recently proposed in literature, and two novel Memetic Algorithms designed for the problem under study. The two proposed algorithms employ different algorithmic philosophies in the field of Memetic Computing. The first algorithm integrates a local search into the operations of the offspring generation, while the second algorithm applies a local search to individuals already generated in the spirit of life-…
A novel abstraction for swarm intelligence: particle field optimization
2016
Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. In essence, within this paradigm, the system is fully defined by a swarm of "particles" each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each …
Simultaneous Airline Scheduling
2008
Currently, there are no solution approaches available to construct and optimize airline schedules within a single model. All existing approaches decompose the problem into smaller and less complex subproblems and solve those subproblems separately. This chapter presents a metaheuristic for simultaneous airline scheduling where several different subproblems are integrated into one single optimization model, except for crew scheduling. The problem-specific metaheuristic uses an adaptive procedure for operator selection to allow an efficient choice between a variety of different operators. Experiments are conducted as proof-of-concept and to calibrate free parameters. Comparing different searc…
Wireless sensor network coverage problem using modified fireworks algorithm
2016
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it …