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Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has bee...
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Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization) and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision, analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
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摘要 :
Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has bee...
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Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization) and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision; analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
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? 2021 Totem Publisher, Inc. All rights reserved.In this paper, a novel fast heuristic optimization algorithm has been proposed that effectively boosts the exploration and exploitation process during the search of a global optimum...
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? 2021 Totem Publisher, Inc. All rights reserved.In this paper, a novel fast heuristic optimization algorithm has been proposed that effectively boosts the exploration and exploitation process during the search of a global optimum in the search region. In this proposed algorithm, a particle position dependent stochastic variable is used to control the exploration and exploitation process. It is also used to reduce the computational time requirement. The search region shrinks efficiently in a continuous manner during successive iterations. The performance of the algorithm has been validated by comparing the simulation results of the particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well-known benchmark functions. The proposed algorithm reduces the computational time around 44.2% compared to others in finding the global optimum point.
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The aim of this paper is to introduce a new hybrid algorithm for bound-constrained optimisation problem combining quantum behaved particle swarm optimisation (QPSO) and binary tournamenting technique. Depending on the different op...
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The aim of this paper is to introduce a new hybrid algorithm for bound-constrained optimisation problem combining quantum behaved particle swarm optimisation (QPSO) and binary tournamenting technique. Depending on the different options of binary tournamenting process, six diverse forms of hybrid algorithm are introduced. Then the efficiency and performance of these hybrid algorithms are investigated through six well known benchmark bound-constrained optimisation problems. Computational results are compared graphically as well as numerically. Finally, this algorithm is utilised to solve four engineering design problems and results are compared with the recent algorithm available in the literature.
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Image enhancement improves visual image quality and plays a crucial part in computer vision and image processing. However, it is the numerous limitations to nonlinear optimisation issues. The goal of the current work is to demonst...
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Image enhancement improves visual image quality and plays a crucial part in computer vision and image processing. However, it is the numerous limitations to nonlinear optimisation issues. The goal of the current work is to demonstrate the adaptability and efficacy of different particle swarm optimisation algorithms in improving the contrast and detail of grayscale images, including PSO, standard PSO (SPSO), weight improved PSO (WIPSO), modified PSO (MPSO), and quantum PSO (QPSO). The optimum result is achieved by maximising the objective function criteria by controlling the transformation function parameters. The performance of the algorithms is measured and assessed through quality metric parameters such as the sum of edge intensities, edge information, entropy, fitness function, detailed variance, and background variance.
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Otsu threshold segmentation is one of the most representative methods for image segmentation. Compared with multilevel threshold segmentation, Otsu method is computationally complex and time-consuming. In this paper, a multilevel ...
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Otsu threshold segmentation is one of the most representative methods for image segmentation. Compared with multilevel threshold segmentation, Otsu method is computationally complex and time-consuming. In this paper, a multilevel thresholding algorithm based on the Quantum Particle Swarm Optimisation (QPSO) is proposed. QPSO combines the classical PSO algorithm with quantum theory. Because of the high effectiveness of QPSO optimisation algorithm, the paper combines this algorithm with Otsu and uses them in multilevel threshold image segmentation. Experiments show that the algorithm can not only realise the image multilevel threshold segmentation, but also make the segmentation more efficient.
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Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been...
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Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard classification datasets which are obtained from UCI machine learning data repository. For verification, the results of the QPSO-SVM algorithm are compared with the standard PSO, and genetic algorithm (GA) which is one of the well-known optimization algorithms. Moreover, the results of QPSO are compared with the grid search, which is a conventional method of searching parameter values. The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. The results also showed lower classification error rates compared with standard PSO and GA algorithms.
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Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios a...
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Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.
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Multiple-objective designs exist in most real-world engineering problems in different disciplines. A multi-objective evolutionary algorithm will face a challenge to obtain a series of compromises of different objectives, called Pa...
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Multiple-objective designs exist in most real-world engineering problems in different disciplines. A multi-objective evolutionary algorithm will face a challenge to obtain a series of compromises of different objectives, called Pareto optimal solutions, and to distribute them uniformly. In this regard, it is essential to keep the balance of local and global search abilities of such algorithms. Quantum-behaved particle swarm optimization (QPSO) is a population-based swarm intelligence algorithm, and differential evolutionary (DE) is another simple population-based stochastic search one for global optimization with real-valued parameters. Although the two optimizers have been successfully employed to solve a wide range of design problems, they also suffer from premature convergence and insufficient diversity in the later searching stages. This is probably due to the insufficient dimensional searching strength, especially for problems with many decision parameters. In this paper, a new multi-objective non-dominated optimal methodology combining QPSO, DE, and tabu search algorithm (QPSO-DET) is proposed to guarantee the balance between the local and global searches. The performances of the proposed QPSO-DET are compared with those of other two widely recognized vector optimizers using different case studies.
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Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-...
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Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. Many clustering algorithms have been used in the field of gene clustering. This paper proposes a new scheme for clustering gene expression datasets based on a modified version of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, known as the Multi-Elitist QPSO (MEQPSO) model. The proposed clustering method also employs a one-step K-means operator to effectively accelerate the convergence speed of the algorithm. The MEQPSO algorithm is tested and compared with some other recently proposed PSO and QPSO variants on a suite of benchmark functions. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of MEQPSO clustering. The performance of MEQPSO clustering algorithm has been extensively compared with several optimization-based algorithms and classical clustering algorithms over several artificial and real gene expression datasets. Our results indicate that MEQPSO clustering algorithm is a promising technique and can be widely used for gene clustering.
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