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Multi-UAV-Borne Surveillance Radar Trajectory Planning Method Based on Imitation Learning. [PDF]
Sensors (Basel)Gao X, Li M, Guan K, Ge J.
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Amplitude-Frequency Response Characteristics and Parameter Optimization of a Bistable Nonlinear Energy Sink Under Wide-Frequency Harmonic Excitation. [PDF]
Materials (Basel)Bao X +5 more
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An improved particle swarm optimization algorithm
Applied Mathematics and Computation, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tiesong Hu, Chongchao Huang
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Improved particle swarm algorithms for global optimization
Applied Mathematics and Computation, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M Montaz Ali, P Kaelo
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Improvement of Particle Swarm Optimization Focusing on Diversity of the Particle Swarm
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020PSO (Particle Swarm Optimization) is attracting attention in recent years to solve the multivariate optimization problems. In PSO, multiple individuals (particles) which records its own position and velocity information are placed in the corresponding search space, and the particle swarm move to discover the optimal solution by sharing information with
Tomohiro Hayashida +3 more
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The improvement of particle swarm optimization
2016 3rd International Conference on Systems and Informatics (ICSAI), 2016Basic particle swarm optimization (PSO) has disadvantages about premature convergence. In order to solve these problems, this paper proposed an improved particle swarm optimization called DPSO. The velocity formula in basic PSO was divided into two parts and the two learning factors were improved.
Zekun Zhou, Bin Jiao
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An improved particle swarm optimization algorithm
2008 IEEE International Conference on Granular Computing, 2008An improved Particle Swarm Optimization (IPSO) algorithm is proposed in this paper. In the algorithm, a premature estimate mechanism is introduced to judge whether the particles accumulate in a small region and tell the probability whether the swarm is trapped in a local optimum.
Lin Lu, Qi Luo, Jun-yong Liu, Chuan Long
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On the improvements of the particle swarm optimization algorithm
Advances in Engineering Software, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ting-Yu Chen, Tzu-Ming Chi
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An improved particle swarm optimization algorithm
2013 Ninth International Conference on Natural Computation (ICNC), 2013Particles can remember some information in an optimization process. They learn by themselves and from other particles, so the next generation can inherit much information from their parents and finally find optimal solutions. But particles are also faced with two problems of stagnating in a local but not global optimum.
Huafen Yang +5 more
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