Search results for "Heuristic"
showing 10 items of 476 documents
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…
Lower and upper bounds for the mixed capacitated arc routing problem
2006
This paper presents a linear formulation, valid inequalities, and a lower bounding procedure for the mixed capacitated arc routing problem (MCARP). Moreover, three constructive heuristics and a memetic algorithm are described. Lower and upper bounds have been compared on two sets of randomly generated instances. Computational results show that the average gaps between lower and upper bounds are 0.51% and 0.33%, respectively.
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 …
A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization
2019
Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve them. However, computationally expensive MaOPs have been scarcely investigated. Typically, surrogate-assisted methods have been used in the literature to tackle computationally expensive problems, but such studies have largely focused on problems with 1–3 objectives. In this paper, we present an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs. The key features of the approach include: 1) the use of mul…
Some Aspects Regarding the Application of the Ant Colony Meta-heuristic to Scheduling Problems
2010
Scheduling is one of the most complex problems that appear in various fields of activity, from industry to scientific research, and have a special place among the optimization problems In our paper we present the results of our computational study i.e an Ant Colony Optimization algorithm for the Resource-Constrained Project Scheduling Problem that uses dynamic pheromone evaporation.