Particle Swarm Optimization Algorithm for Multisalesman Problem with Time and Capacity Constraints

Wang

2019

Massive multiple input multiple output (MIMO) has drawn intensive attention of researchers for solving huge data transmissions in the fifth generation wireless communication. Antennas aligning in array bring in advantages for interference reduction of incoming wave signals and interference offset among antenna elements. The design of antenna array is studied in this paper. The design is turned into a maximization problem. A contraction adaptive particle swarm optimization (CAPSO) method is proposed to solve the problem. Different from previous methods, CAPSO is based on a contraction factor which limits the variation neighborhood during the solution searching process. The adaptive technique can tune the searching range of CAPSO. Simulations are reported comparing the CAPSO method with other methods. Convergence rate is analyzed through two toy functions. CAPSO shows a fast convergence property. Then it is used to solve antenna array design. The CAPSO method shows good performance for different numbers of array elements.

Section: Introductionmentioning

confidence: 99%

Antenna array design by a contraction adaptive particle swarm optimization algorithm

Wang

2019

“…Yu et al studied a decomposition method for bound constrained optimization problems [15]. Stochastic algorithms such as evolutionary algorithms, particle swarm optimization [16,17], and artificial bee colony converge slower than deterministic ones [18][19][20] though they take objective functions as black-box, which makes these algorithms applicable to nearly all kinds of antenna design problems. Parallel execution of optimization algorithms could greatly save computational time [21,22].…”

Section: Introductionmentioning

confidence: 99%

Tuning rectangular microstrip antenna through scan and zoom algorithm

2018

Microstrip antennas have been widely studied and can be applied to a large number of wide frequency domain in wireless communication. Microstrip antennas are featured with light weight, small volume, and low cost advantages. Planar or spatial structures could substantially affect the frequency reliability and sidelobe level of microstrip antennas. Designing high performance microstrip antennas requires the tuning of the length and width of microstrip patch, the equivalent permittivity, the feed position of the coaxial line, and so on. Although electromagnetic theory could provide a solution for antenna design, such solution is probably not optimal solution due to complex environment and the affection of multiple factors. This paper attempts to tuning the design of microstrip antennas by using scan and zoom algorithm. It does not require derivative information of antenna model. This algorithm consists of search space scan and search domain zoom stages. The effectiveness of the scan and zoom algorithm is firstly tested on mathematical functions. The algorithm is then verified on rectangular microstrip antenna design. Compared with theoretical solution, the novel algorithm attains better design solution. The algorithm can also be easily extended to tackle other antenna design problems.

“…A large number of iterations is needed for standard PSO algorithm to obtain a satisfactory solution [27]. This is not acceptable for users as antenna simulation often takes a long time.…”

Section: Introductionmentioning

confidence: 99%

Thinning of antenna array via adaptive memetic particle swarm optimization

2017

Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication.Proper antenna array design can reduce interference among different signals and generate desirable beamforming. Sparse antenna array is able to form narrower beam with lower sidelobe than equally spaced antenna array given the same number of array elements. However, determining the position of elements is non-deterministic polynomial-time hard. To effectively solve such problem, this paper proposes adaptive memetic particle swarm optimization (AMPSO) algorithm. The algorithm adaptively tunes algorithmic parameters of particle swarm optimization (PSO). Moreover, crossover operator is added to enhance local exploiting search information of PSO. Sparse antenna array design is modeled as a minimization by thinning method. It is then tackled by the proposed algorithm. In terms of peak sidelobe level, the AMPSO algorithm shows good performance compared with PSO and genetic algorithm.