An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modelling

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

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Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

International Journal of Antennas and Propagation ◽

10.1155/2016/3592973 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Weijie Xia ◽

Xue Jin ◽

Fawang Dou

Keyword(s):

Particle Swarm Optimization ◽

Convex Optimization ◽

Optimization Algorithm ◽

Performance Optimization ◽

Hybrid Algorithm ◽

Particle Swarm ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Two Factors ◽

Imaging Sonar

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms.

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Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410017696110 ◽

2017 ◽

Vol 232 (8) ◽

pp. 1571-1583 ◽

Cited By ~ 2

Author(s):

Yongbin Sun ◽

Haibin Duan

Keyword(s):

Particle Swarm Optimization ◽

Lateral Inhibition ◽

Image Matching ◽

Hybrid Algorithm ◽

Particle Swarm ◽

Matching Problem ◽

Aerial Refueling ◽

Swarm Optimization ◽

Swarm Intelligence Algorithm ◽

Computation Speed

Autonomous aerial refueling (AAR) is an essential application of unmanned aerial vehicles for both military and civilian domains. In this paper, a hybrid algorithm of the pigeon-inspired optimization (PIO) and lateral inhibition (LI), called LI-PIO, is proposed for image matching problem of AAR. LI is adopted for image pre-processing to enhance the edges and contrast of images. PIO, inspired from the homing characteristics of pigeons, is a novel bio-inspired swarm intelligence algorithm. To demonstrate the effectiveness and feasibility of our proposed algorithm, we make extensive comparative experiments with particle swarm optimization (PSO), particle swarm optimization based on lateral inhibition (LI-PSO), and PIO. It can be concluded from the experimental results that our proposed LI-PIO has excellent performances for image matching problem of AAR, especially in convergent rate and computation speed.

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