HUI WANG - Academia.edu (original) (raw)
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Papers by HUI WANG
Advances in Computation and Intelligence
The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in whi... more The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group's previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a fast particle swarm optimization (FPSO) algorithm is proposed by combining PSO and the Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. FPSO has been compared with another improved PSO called AMPSO [12] on a set of benchmark functions. The results show that FPSO is much faster than AMPSO on all the test functions.
IEEE Transactions on Magnetics, 2012
This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to d... more This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to design broadband optimal Yagi-Uda antennas. A multi-objective problem is formulated to achieve maximum directivity, minimum voltage standing wave ratio and maximum front-to-back ratio. The algorithm was applied to the design of optimal 3 to 10 elements Yagi-Uda antennas, whose optimal Pareto fronts are provided in a single picture. The multi-objective problem is decomposed by Chebyshev decomposition, and it is solved by differential evolution (DE) and Gaussian mutation operators in order to provide a better approximation of the Pareto front. The results show that the implemented MOEA/D is efficient for designing Yagi-Uda antennas.
Advances in Computation and Intelligence
The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in whi... more The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group's previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a fast particle swarm optimization (FPSO) algorithm is proposed by combining PSO and the Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. FPSO has been compared with another improved PSO called AMPSO [12] on a set of benchmark functions. The results show that FPSO is much faster than AMPSO on all the test functions.
IEEE Transactions on Magnetics, 2012
This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to d... more This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to design broadband optimal Yagi-Uda antennas. A multi-objective problem is formulated to achieve maximum directivity, minimum voltage standing wave ratio and maximum front-to-back ratio. The algorithm was applied to the design of optimal 3 to 10 elements Yagi-Uda antennas, whose optimal Pareto fronts are provided in a single picture. The multi-objective problem is decomposed by Chebyshev decomposition, and it is solved by differential evolution (DE) and Gaussian mutation operators in order to provide a better approximation of the Pareto front. The results show that the implemented MOEA/D is efficient for designing Yagi-Uda antennas.