Artificial physics optimization algorithm (APO) is a new swarm intelligence algorithm to solve global optimization problem based on Physicomimetics framework. An n order diagonal matrix of shrinkage coefficient is int...
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Artificial physics optimization algorithm (APO) is a new swarm intelligence algorithm to solve global optimization problem based on Physicomimetics framework. An n order diagonal matrix of shrinkage coefficient is introduced to ensure that each individual is within the decision space. Multi-dimensional search method is merged into the vector model of APO to improve the local exploitation capability of vector APO. The simulation results confirm that the performance of the hybrid vector APO with multi-dimensional search method is effective.
Artificial physics optimization algorithm (APO) is used to solve constrained optimization problem. A n order diagonal matrix of shrinkage coefficient is introduced to ensure that each individual is within the decision...
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Artificial physics optimization algorithm (APO) is used to solve constrained optimization problem. A n order diagonal matrix of shrinkage coefficient is introduced to ensure that each individual is within the decision space. Multi-dimensional search method is merged into the vector model of APO to ensure that the moving of the whole population is limited in the feasible region. The simulation results confirm that the performance of the hybrid vector APO with multi-dimensional search method is effective.
Velocity threshold v max is an important parameter of Artificial physics optimization. Different from other parameters, it affects the algorithm performance by restricting the moving size and direction of each partic...
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Velocity threshold v max is an important parameter of Artificial physics optimization. Different from other parameters, it affects the algorithm performance by restricting the moving size and direction of each particle. Because of the complex optimisation problems, the proper v max setting may provide a reasonable solution within an allowed generation. However, up to now, there are only few scholars who are concerned in this problem. Therefore, in this paper, the authors investigate two selection principles of v max (a canstant v max and an adaptive v max ) with high dimension on numerical optimisation problems. To make a deep insight, the test suit consists of three different type benchmarks: unimodel, multi-modal functions with a few local optima and multi-modal functions with many local optima. simulation results show an adaptive v max may generally obtain the satisfied solution within the allowed iterations.
Clustering analysis is primarily concerned with the classification of data points into different clusters. Estimation of distribution algorithms (EDAs) uses machine learning techniques to solve optimisation problems b...
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In this paper, a new stochastic optimization algorithm is introduced to simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phe...
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An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertaint...
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The optimal coverage problem plays an important role in wireless sensor networks. In this paper a new swarm intelligent technique, Social Emotional Optimisation Algorithm (SEOA), is used to solve the problem based on ...
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In this paper, the scattering principium of airborne phased array antenna is discussed, and the antenna mode RCS of the two-dimensional array is estimated. Based on theoretical calculations, broadband matching, inclin...
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ISBN:
(纸本)9781424482658
In this paper, the scattering principium of airborne phased array antenna is discussed, and the antenna mode RCS of the two-dimensional array is estimated. Based on theoretical calculations, broadband matching, incline, manufacturing tolerances and other RCS reduction measures are researched. The results show that these stealth approaches have an obviously effect on the RCS reduction.
The copula estimation of distribution algorithm is used in the work of codebook design, in which the selected population is the training vectors who coding with the concerned codeword. In each generation, the new code...
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The copula estimation of distribution algorithm is used in the work of codebook design, in which the selected population is the training vectors who coding with the concerned codeword. In each generation, the new codeword are generated by a copula and the estimated margins according to the selected population. The experimental results show that the proposed algorithm performs better than some classic codebook design algorithm such as LBG, ANT based algorithms, codebook-based tuba search algorithm and partitioning-based tuba search algorithm.
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