Phased Array Synthesis Using Modified Particle Swarm Optimization

Zaman, Mohammad Asif; Mamun, Sayed Ashraf; Gaffar, Md.; Choudhury, Sajid; Alam, Md. Mushfiqul; Matin, Md. Abdul

doi:10.25103/jestr.041.10

Cited by 12 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to several studied the value of inertia weight w can be a constant wo=1, 0.79; [19], also wo can be linearly damped function with iterations starting at 0.9 and decreasing linearly to 0.4 at the last iteration [12], or depending on [20], wo can be exponentially decay function starting at 0.9 and decreasing to 0.4 at last iteration. After we define the required updating velocity parameters, the position matrix is updated at each iteration according to:…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Design of Optimal Beamforming using Particles Swarm Optimization

Khawaldeh¹,

Khawaldeh²

2017

IJCA

View full text Add to dashboard Cite

Beamforming is a signal processing technique by which an array of receivers sensitive to signals from all directions can be processed to form one larger more sensitive receiver that can identify which direction signals originate. In this paper the narrowband beamforming and wideband beamforming using Tapped delay line will be investigated. Some applications in wireless communication systems need to design beamformer with a specified response like a sidelobe level less than threshold level or forming a null response points in interference direction or jamming frequency. So we need to design optimal beamforming using optimization technique, to achieve a desired response. In this paper the common global optimization technique like Particle Swarm Optimization (PSO) will be discussed and will used to design a different examples on narrowband and wideband beamforming.

show abstract

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Design of Optimal Beamforming using Particles Swarm Optimization

Khawaldeh¹,

Khawaldeh²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…To simplify this process, the equation constraints are transformed into inequality constraints using the slack variable method. These problems have been investigated in numerous previous studies [7], [8], [9].…”

Section: Co Principlementioning

confidence: 99%

Multidisciplinary Design and Collaborative Optimization for Excavator Backhoe Device

qiu¹,

wen²,

Perlurst³

2014

JESTR

View full text Add to dashboard Cite

The excavator working device is a typical mechanical system of electromechanical liquid that is complex. Traditional optimization design methods are difficult to get global optimized results of excavator backhoe device through the serial mode of "mechanism-load-structure". Thus, the theory of parallel collaborative optimization (CO) is applied. To establish a sophisticated CO model of the backhoe device, a certain excavator is investigated as a sample multidisciplinary CO (MDCO) design. To generate the CO model, an improved optimization algorithm called the particle swarm-genetic algorithm (PS-GA)is proposed. To verify the MDCO design of the excavator backhoe device, a parameterized virtual prototype (VP) of the backhoe device is established in ADAMS. This VP is optimized by applying the MDCO design results to the parameterized VP. The VP of the backhoe device is also optimized by a single discipline when the optimization results from a single discipline are inputted into the parameterized VP. Both optimized VPs are simulated under similar conditions, and results show that in the MDCO design, the arm crowd force of the backhoe device is 8.1% stronger than that in the design optimized by a single discipline under constant power and oil pressure conditions. Similarly, breakout force increased by approximately 8.3%. The quality (volume) of the entire backhoe device decreased by 9.5%; however, the maximum stress of each characteristic partition changed only slightly. Therefore, the MDCO design effectively and practically addresses problems regarding the optimization of the design of complex mechanical systems.

show abstract

“…However, exact control of phase and magnitude of excitation current of array elements requires complex and expensive electronic circuitry [4]. In case of phased arrays, where the direction of the main beam needs to be controlled electronically in real time [7], uses of such electronic circuits are unavoidable. However, in many applications, a dynamic control of array radiation pattern is not required.…”

Section: Introductionmentioning

confidence: 99%

“…Linear array of identical radiating elements are one of the common type of antenna arrays [4,6]. Design method of linear uniformly spaced antenna arrays are widely covered in literature [5][6][7][8]. Most of these methods employ a heuristic optimization algorithm to find the phase and/or magnitude of the excitation currents of the array elements while keeping the separation between the array elements uniform.…”

Section: Introductionmentioning

confidence: 99%

Nonuniformly Spaced Linear Antenna Array Design Using Firefly Algorithm

Zaman

Matin

2012

International Journal of Microwave Science and Technology

View full text Add to dashboard Cite

A nonuniformly spaced linear antenna array with broadside radiation characteristics is synthesized using firefly algorithm and particle swarm optimization. The objective of the work is to find the optimum spacing between the radiating antenna elements which will create a predefined arbitrary radiation pattern. The excitation amplitudes of all the antenna elements are assumed to be constant. The optimum spacing between the array elements are obtained using firefly algorithm. The minimum allowed distance between the antenna elements is defined in such a way that mutual coupling between the elements can be ignored. Numerical analysis is performed to calculate the far-field radiation characteristics of the array. Two numerical examples are shown to form two different desired predefined radiation patterns. The performance of the firefly algorithm and particle swarm optimization is compared in terms of convergence rate and global best solution achieved. The performances of the optimized nonuniformly spaced arrays are analyzed. Finally, contour plots of the radiated power from the optimized array in the horizontal plane and vertical plane in the far-field region are provided.

show abstract

Phased Array Synthesis Using Modified Particle Swarm Optimization

Cited by 12 publications

References 9 publications

Design of Optimal Beamforming using Particles Swarm Optimization

Design of Optimal Beamforming using Particles Swarm Optimization

Multidisciplinary Design and Collaborative Optimization for Excavator Backhoe Device

Nonuniformly Spaced Linear Antenna Array Design Using Firefly Algorithm

Contact Info

Product

Resources

About