qpso algorithm (qpso) is introduced to design a collision-free trajectory for planar redundant manipulators. Chaotic sequences instead of random sequences are used in qpso to diversify the qpso population. Kinematics ...
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ISBN:
(纸本)9781424467129
qpso algorithm (qpso) is introduced to design a collision-free trajectory for planar redundant manipulators. Chaotic sequences instead of random sequences are used in qpso to diversify the qpso population. Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomials are used to describe the segment of the trajectory. qpso optimizes the trajectory and ensures obstacle avoidance can be achieved. Simulations are carried out for different obstacles to prove the validity of the proposed algorithm. Different test data generated by GA and qpso are provided with a tabular comparison. Simulation studies show qpso has potential online usage in engineering and distinct fast computation speed compared with GA.
The applying of qpso-FISH algorithm in the wireless senor network are mainly studied in this *** sensor network is an energy-constrain network,coverage efficiency and power consumption are two important performance **...
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The applying of qpso-FISH algorithm in the wireless senor network are mainly studied in this *** sensor network is an energy-constrain network,coverage efficiency and power consumption are two important performance *** the network coverage and minimizing the number of working nodes are network optimization *** an optimal model for coverage in wireless sensor *** the characteristics of parallel,fast coveragence and fast local search speed,propose a coverage optimization strategy based on qpso-FISH *** results of simulation show that the algorithm can get an optimal set of nodes and improve the real-time capability of nodes scheduling.
With regard to modern warfare, the environmental information is changing and it's difficult to obtain the global environmental information in advance, so real-time flight route planning capabilities of unmanned ae...
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ISBN:
(纸本)9783642112751;9783642112768
With regard to modern warfare, the environmental information is changing and it's difficult to obtain the global environmental information in advance, so real-time flight route planning capabilities of unmanned aero vehicles (UAV) is required. Quantum Particle Swarm Optimization (qpso) is introduced to solve this optimization problem. Meanwhile, According to the threats distribution of terrain obstacles, adversarial defense radar sites and unexpected surface-to-air missile (SAM) sites, Surface of Minimum Risk (SMR) is introduced and used to form the searching space. The objective function for the proposed qpso is to minimizing traveling time and distance, while exceeding a minimum pre-defined turning radius, without collision with any obstacle in the flying workspace. Quadrinomial and quintic polynomials are used to approach the horizon projection of the 3-D route and this simplifies the original problem to a two dimension optimization problem, thus the complexity of the optimization problem is decreased, efficiency is improved. The simulation results show that this method can meet online path planning.
To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （qpso） alg...
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To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （qpso） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of qpso algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.
Quantum-behaved particle swarm optimization (qpso)is an algodthm,which has good global optimization effects,and simple *** it has inherent *** Programmable Gate Array (FPGA)with a fine-grained parallel computing capab...
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Quantum-behaved particle swarm optimization (qpso)is an algodthm,which has good global optimization effects,and simple *** it has inherent *** Programmable Gate Array (FPGA)with a fine-grained parallel computing capabilities,is suitable as qpso high computing *** paper designed a high-performance computing platform for qpso,and realized in XILINX company's SPARTAN-Ⅲ*** whole design structure of the system is modular,and use pipeline technology to optimize the *** testing some reliable benchmark functions,the computing platform that can be effective in reducing the running time and improve qpso practical value.
A technique for Fuzzy Cognitive Maps learning,which is based on the Quantum-behaved Particle Swarm Optimization algorithm,is *** proposed approach is used for updating the nonzero weight values that lead the Fuzzy Cog...
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A technique for Fuzzy Cognitive Maps learning,which is based on the Quantum-behaved Particle Swarm Optimization algorithm,is *** proposed approach is used for updating the nonzero weight values that lead the Fuzzy Cognitive Map to desired steady *** workings of the approach are applied to an industrial control *** results support the claim that the proposed technique is a promising methodology for Fuzzy Cognitive Maps learning,and the methodology is effective and efficient.
This paper proposes an image fusion approach based on qpso *** formulate the image fusion problem as an optimization problem and adopt Quantum-behaved Particle Swarm Optimization algorithm to solve the problem. Not on...
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This paper proposes an image fusion approach based on qpso *** formulate the image fusion problem as an optimization problem and adopt Quantum-behaved Particle Swarm Optimization algorithm to solve the problem. Not only qpso has less parameter to control,but also does its sampling space at each iteration cover the whole solution *** qpso can find the best solution quickly and guarantee to be global *** this paper,another two methods,Genetic algorithm(GA) and Particle Swarm Optimization(PSO) are tested for performance comparison with qpso,and the result show the good efficiency of qpso algorithms to image fusion.
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