18 results with keyword: 'distributed centralized particle swarm optimization learning flocking behaviors'
(a) Performance measured for 20 evaluations per controller with the global metric with real robots and in simulation for comparison of the selected controllers for: global
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Performance measured with the global metric in simulation for the 20 solutions found with (a) global homogeneous (centralized) learning and (b) local heterogeneous
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Keywords: Comprehensive Learning Particle Swarm Optimization (CLPSO), Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Comprehensive Learning
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Keywords: backhauling, centralized, coordinated multipoint, convex optimization, de- centralized, efficient backhauling, joint transmission, particle swarm optimization, pre-
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Lee and Myung [20] used a distributed version of PSO for online optimization of the individual trajectories of robots running model predictive control for flocking by using
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Ensemble learning particle swarm optimization for real time UWB indoor localization RESEARCH Open Access Ensemble learning particle swarm optimization for real time UWB indoor
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This paper offers suggestions as to how the seminars on teaching and learning, learning styles, objectives, planning a class, questioning, and classroom assessment techniques can
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Licensed under Creative Common Page 17 On the factors that influence a bank workers decision to demand for life insurance or not, gender, marital status,
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• Designed in 1987 by Internet Engineering Task Force (IETF) to send and receive management and status information across networks.. • Most widely used network management protocol
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In this paper, we proved some fixed point theorems in intuitionistic fuzzy metric spaces applying the properties of weakly compatible mapping and satisfying the concept of
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By adopting modified particle swarm optimization (MPSO), improved particle swarm optimization (IPSO), self-organizing particle swarm optimization (SOH-PSO),
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These methods include the social behavior of bird flocking and fish schooling such as the Particle Swarm Optimization (PSO) algorithm [1], the cooperative behavior
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These are original particle swarm optimization (PSO) [ 12 ], the particle evolutionary swarm optimization (PESO) [ 12 ], the constrained handling method of particle swarm
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In this study, a novel PSO using fitness feedback mechanism and quadratic inertia weight is proposed, which aims to enable the PSO to gain the abilities of good convergence speed
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To further improve the performance of PSO, this paper develops a genetic learning scheme that applies GA for exem- plar construction. As illustrated in Fig. The main loop of
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There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader
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This way, a good particle is more likely to perform the arithmetic crossover between its pbest position P i and the gbest position G. By integrating the information of the global
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