The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO ...
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The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.
The article presents an example of the use of functional series for the analysis of nonlinear systems for discrete time signals. The homogeneous operator is defined and it is decomposed into three component operators:...
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The article presents an example of the use of functional series for the analysis of nonlinear systems for discrete time signals. The homogeneous operator is defined and it is decomposed into three component operators: the multiplying operator, the convolution operator and the alignment operator. An important case from a practical point of view is considered - a cascade connection of two polynomial systems. A new, binary algorithm for determining the sequence of complex kernels of cascade from two sequences of kernels of component systems is presented. Due to its simplicity, it can be used during iterative processes in the analysis of nonlinear systems (e.g. feedback systems)..
The binary Artificial Electric Field Algorithm (BAEFA) is the binary version of the Artificial Electric Field Algorithm (AEFA) scheme. In this article, two transfer functions are proposed to convert the real-valued ca...
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ISBN:
(纸本)9781665487689
The binary Artificial Electric Field Algorithm (BAEFA) is the binary version of the Artificial Electric Field Algorithm (AEFA) scheme. In this article, two transfer functions are proposed to convert the real-valued candidate solutions of the original AEFA into binary values. Additionally, an XOR operator is used to enhance the effectiveness and wider applicability of the AEFA scheme in the binary format. This article also presents a theoretical analysis of the proposed work that makes recommendations for the conditions in which the velocity term of AEFA assumes the values in the positive and negative direction. Furthermore, the proposed scheme is used to solve real-world feature selection problems. The proposed scheme gives effective results that are superior to other competent algorithms in most cases.
In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid binary Bat Enhanced...
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In this paper, a new hybrid binary version of Genetic algorithm (GA) and enhanced particle swarm optimization (PSO) algorithm is presented in order to solve feature selection (FS) problem. The proposed algorithm is ca...
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The feature subset selection and parameter optimization influence the classification accuracy of support vector machines (SVM) significantly. In order to solve the problems and improve the performance of SVM for class...
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ISBN:
(纸本)9781509063529
The feature subset selection and parameter optimization influence the classification accuracy of support vector machines (SVM) significantly. In order to solve the problems and improve the performance of SVM for classification, a new proposed chaos-optimized binary version of PSOGSA which is the hybrid of particle swarm optimization (PSO) and gravitational search algorithm (GSA), termed CBPSOGSA, is developed in this paper to build a novel hybrid system (CBPSOGSA-SVM) for classification. In this system, features and SVM parameters are encoded together to form the binary code strings and the feature subset and SVM parameters are optimized simultaneously. A proper fitness function is also designed to convert this multi-objective optimization problem to be single objective, meanwhile, evaluate the binary code strings produced by CBPSOGSA. Eight standard datasets are employed in experiments to validate the performance of the proposed CBPSOGSA-SVM hybrid system for classification. The binary version of both PSO and PSOGSA are also used to do comparisons. The results of experiments demonstrate that the proposed CBPSOGSA-SVM hybrid system for classification is effective and has better performance and stronger capability of searching for the global best solutions.
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