With recent increase in energy consumption as well as reduction of fossil fuels, employing new methods for generation of green energy in smart grids, such as wind energy, is of great interest for governments. That is ...
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ISBN:
(纸本)9781479952649
With recent increase in energy consumption as well as reduction of fossil fuels, employing new methods for generation of green energy in smart grids, such as wind energy, is of great interest for governments. That is why expanding of wind turbine farms is a priority in many countries. One of the most important parameters in design and implementation of such farms is optimum selection of wind turbine farm location in a way that the corresponding constraints are met. This paper introduces a new optimization algorithm based on the oppositionbased ant colony optimization (OACO) algorithm for this aim. Analyzes of simulation results demonstrate performance of the proposed method for optimum localization of wind turbine farms in Saudi Arabia case study.
In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization ph...
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In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm.
Large scale neural networks have many hundreds or thousands of parameters (weights and biases) to learn, and as a result tend to have very long training times. Small scale networks can be trained quickly by using seco...
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ISBN:
(纸本)9781424435494
Large scale neural networks have many hundreds or thousands of parameters (weights and biases) to learn, and as a result tend to have very long training times. Small scale networks can be trained quickly by using second-order information, but these fail for large architectures due to high computational cost. Other approaches employ local search strategies, which also add to the computational cost. In this paper we present a simple method, based on opposite transfer functions which greatly improve the convergence rate and accuracy of gradient-based learning algorithms. We use two variants of the backpropagation algorithm and common benchmark data to highlight the improvements. We find statistically significant improvements in both converegence speed and accuracy.
Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic,symmetry refers to the invariance of an object to some transformation, or set of *** one searches for, and uses infor...
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Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic,symmetry refers to the invariance of an object to some transformation, or set of *** one searches for, and uses information concerning an existing symmetry within given data,structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, thequestion being asked is whether only existing symmetries can be useful, or whether changingreference to an intuition-based definition of symmetry over the evaluation function can also be ofuse. Within the context of optimization, symmetry induction as defined in this thesis will have theeffect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search ***, conditions when the search space can have a symmetry induced on it are examined. Theidea of a neighborhood structure then leads to the idea of opposition-based computing which aimsto induce a symmetry of the evaluation function. In this context, the search space can be seen ashaving a symmetry imposed on it. To be useful, it is shown that an opposite map must be definedsuch that it equates elements of the search space which have a relatively large difference in theirrespective evaluations. Using this idea a general framework for employing opposition-based ideasis proposed. To show the efficacy of these ideas, the framework is applied to popular computationalintelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution andneural network *** first example application focuses on simulated annealing, a popular Monte Carlo optimizationalgorithm. At a given iteration, symmetry is induced on the system by considering oppositeneighbors. Using this technique, a temporary symmetry over the neighborhood region
In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amo...
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In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population, We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented prob-abilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning. (C) 2008 Elsevier Inc. All rights reserved.
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