Thinned Concentric Circular Array Antenna Synthesis Using Seeker Optimization Algorithm

In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a novel particle swarm optimization algorithm (NPSO) to finally determine the global optimal CCAA design. Real coded Genetic Algorithm (RGA) is also employed for comparative optimization but it proves to be suboptimal. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the three-ring CCAA containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design with global minimum SLL (-19.69dB) determined by NPSO.

Non-uniform Concentric Circular Antenna Array Design using IPSO Technique for Side Lobe Reduction

ABSTRACTDue to the strong non-linear relationship between the array factor and the array elements, concentric circular antenna array (CCAA) synthesis problem is challenging. Nature-inspired optimisation techniques have been playing an important role in solving array synthesis problems. Dragonfly algorithm (DA) is a novel nature-inspired optimisation technique which is based on the static and dynamic swarming behaviours of dragonflies in nature. This paper presents the design of CCAAs to get low sidelobes using DA. The effectiveness of the proposed DA is investigated in two different (with and without centre element) cases of two three-ring (having 4-, 6-, 8-element or 8-, 10-, 12-element) CCAA design. The radiation pattern of each design cases is obtained by finding optimal excitation weights of the array elements using DA. Simulation results show that the proposed algorithm outperforms the other state-of-the-art techniques (symbiotic organisms search, biogeography-based optimisation, sequential quadratic programming, opposition-based gravitational search algorithm, cat swarm optimisation, firefly algorithm, evolutionary programming) for all design cases. DA can be a promising technique for electromagnetic problems.

Design of an advanced electric power distribution systems using seeker optimization algorithm

In order to overcome the drawbacks of falling into local extremum and lower optimization precision of standard particle swarm optimization (PSO) algorithm, multipopulation strategy, adaptive dynamic adjustment strategy and co-evolution mode are introduced into the standard PSO algorithm in order to propose an improved coevolutionary PSO(MPACEPSO) algorithm based on multi-strategy evolution mode and multi-population co-evolutionary mechanism. In the evolutionary process of MPACEPSO algorithm, the multi-population strategy is used to divide the population into several subpopulations, which use different co-evolutionary model to evolve. These sub-populations are influenced and promoted each other in order to realize the exchange of information and co-evolution among the sub-populations, improve the convergence speed and search precision of MPACEPSO algorithm, and effectively suppress the appearance of the local optimum. The adaptive dynamic adjustment strategy of inertia weight is used to keep the diversity of population, reduce the probability of falling into the local extremum. Finally, the ZDT functions are selected to test the optimization performance of proposed MPACEPSO algorithm. The experimental results show that the proposed MPACEPSO algorithm has faster convergence speed, stronger global search ability, higher solving precision and better dynamic optimization performance. The experimental result analysis shows that the proposed MPACEPSO algorithm is insensitive to parameters and easy to be used in solving the complex optimization problems.

In this paper, the design of non-uniform concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level reduction is presented. The newly-proposed global evolutionary optimization methods; namely: the biogeography based optimization (BBO) is used to determine a set of weights that provides a radiation pattern with optimum side lobe level reduction with the constraint of a fixed major lobe beamwidth. The results obtained from BBO are compared with those obtained using the Matlab function Fmincon which uses a sequential quadratic programming (SQP) method. The comparison shows that the design of non-uniformly excited CCAAs using SQP method provides a side lobe level reduction that is better than that obtained using global stochastic optimization methods, indicating that global optimization techniques are not really needed in this problem.

In this paper the optimal performance of time modulated nine-ring concentric circular antenna array with isotropic elements has been studied based on an evolutionary optimization algorithm hybridized with local heuristic search called memetic firefly algorithm (MFA). The firefly algorithm has been applied followed by Nelder–Mead simplex method for the local heuristic search to achieve the optimal fine tuning. Other algorithms like real coded genetic algorithm (RGA) and particle swarm optimization (PSO) have been used for the comparison purpose. The comparisons among the algorithms have been made with two case studies as Case-1 and Case-2, and with two different fitness functions $$(f_{{ fitness}1}, f_{{ fitness}2})$$ and three control parameters like inter-element uniform/non-uniform spacing in rings, inter-ring radii and the switching-on times of rings. The simulation results show that the MFA outperforms RGA and PSO for both the cases Case-1, Case-2 and $$f_{{ fitness}1}$$ , $$f_{{ fitness}2}$$ , respectively with respect to better side lobe level (SLL). The fitness function $$f_{{ fitness}2}$$ is better than the $$f_{{ fitness}1}$$ with respect to sideband level. Apart from this, powers radiated at the centre/fundamental frequency and the first two sideband frequencies, and dynamic efficiency have been computed. It is found that power radiated by any sideband frequency is much less as compared to the power radiated at the centre frequency. It has been observed that as the sideband frequency increases, SBL decreases to the greater extent as compared to SLL. As per authors’ knowledge there is a little research contribution by any other previous researcher regarding numerical computation of radiation characteristics as SBLs, powers radiated at the fundamental frequency and its two sideband frequencies, directivity, and dynamic efficiency for time-modulated CCAA.

In many communication applications like radar and point to point micro wave links, narrow beamwidth and low side lobe level beams are required. Trying to generate narrow beams using conventional antenna synthesis methods results in higher side lobe levels and trying to generate low side lobe beams results in broader beams. Dolph chebychev linear array method is a compromise between SLL and FNBW. In this method, for a given SLL, the narrowest beam width is obtained and for a given beamwidth the smallest SLL is obtained. But the smallest SLL for the narrowest beamwidth is not obtained. In this paper an effort is made to get the narrowest main beamwidth without significantly increasing SLL and the smallest SLL is obtained without significantly broadening the beamwidth using random stochastic methods like Particle swarm optimization (PSO) and Genetic algorithm (GA). Results obtained with PSO and results obtained with GA are compared.

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results.

Enhancement of performance of antenna array in terms of directive characteristics requires simultaneous control of both side-lobe level (SLL) and beam width (BW). The population-based evolutionary computing techniques are best suited for such array synthesis problems. In this paper, a novel evolutionary computing tool known as flower pollination algorithm (FPA) is applied to linear array synthesis problem. In addition, the performance of both amplitude only and amplitude–spacing-based methods is assessed. The proposed method estimates the weights of each objective so that the generated radiation patterns with desired SLL and BW are produced. The performance and the efficiency of FPA-based method are evaluated and also compared with those obtained using genetic algorithm and particle swarm optimization. The problem of synthesizing patterns with narrow and wide nulls is also investigated. Multiple narrow band nulls as well wide nulls are produced using the FPA with amplitude only method. The investigation has been made for both sum and difference patterns.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

This paper compares the performances of a number of uniform circular array (UCA) configurations for phased array antennas. A UCA geometry is targeted due to its symmetrical configuration which enables the phased array antenna to scan azimuthally with minimal changes in its beam width and sidelobe levels. Each UCA configuration consists of 19 isotropic elements. Particle Swarm Optimization (PSO) is used to calculate the complex weights of the antenna array elements in order to adapt the antenna to the changing environments. Comparisons are made in the context of adaptive beamforming properties and Signal to Interference Ratio (SIR). The results obtained suggest that a planar uniform hexagonal array PUHA (1:6:12) is suitable for high resolution applications as its sidelobe levels are the lowest compared to the other geometries.

This paper presents the application of ecology based Flower Pollination Algorithms (FPA) on the antenna array. Ecology based FPA method applied on linear antenna array. The design of linear antenna array consider the optimization of amplitude excitation of each element for the desired pattern in elevation plane 0° to 180°to achieve better directivity , good half power beam width (HPBW) and reduced side lobe level (SLL).Finally, FPA is compared with Particle Swarm Optimization (PSO). Simulation Results portrait that the proposed optimization technique proved its superiority over PSO.

Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach

A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination