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Opposition-based gravitational search algorithm for synthesis circular and concentric circular antenna arrays

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Abstract

In this paper, a population based evolutionary optimization methodology, called Opposition-based Gravitational Search Algorithm (OGSA), is applied for optimal designs of three non-uniform single-ring Circular Antenna Arrays (CAA) of set 8, 10 and 12 elements and non-uniform 3-ring Concentric Circular Antenna Array (CCAA). Two 3-ring concentric circular antenna arrays having sets of 4-, 6-, 8- elements and 8-, 10-, 12- elements, with and without center element, are considered. The algorithm is used to determine an optimal set of current excitation weights and antenna inter-element separations for circular antenna array of 8, 10, and 12 elements and optimal current excitation weights for CCAA, respectively. OGSA provides optimal radiation pattern with maximum Side Lobe Level (SLL) reduction and First Null Beam Width (FNBW) reduction with improved directivity for CAA and maximum reduction of SLL for CCAA, respectively. OGSA is developed on the primary foundation of Gravitational Search Algorithm (GSA) blended with the concept of opposition based approach. Simulation results show a considerable improvement of radiation pattern with respect to the corresponding uniform cases of both the types of antenna array and those of some recent literature reported in this paper. Finally, comparison of accuracies of the proposed algorithm is performed by t-test calculation.

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... Moreover, in this work, four different CCAA design cases were used to study DA efficiency. Then, the results evaluated against approaches like BBO [44], SOS [45], SQP [44], CSO [46], OGSA [47], EP [48], and FA [49]. The proposed work utilized two three-ring designs; CCAA with 4-, 6-, 8-plus 8-, 10-, 12-, besides two cases well-thought-out for each model: CCAA without, and with the center component. ...
... However, because of the requirement of the cases, personal potentiality, and other assigned cases, the necessary time for each group to solve the same case was not the same. To find the best solution for the proposed work, DA and the Firefly Algorithm (FA) was utilized [47]. (1, 90). ...
... Nevertheless, the results from the references mentioned in the paper are another piece of evidence for the speed of convergence of the DA. Reference [47] utilized the DA and FA for optimizing the same problem. The results showed the high convergence of the DA. ...
Preprint
p> The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems. </p
... Moreover, in this work, four different CCAA design cases were used to study DA efficiency. Then, the results evaluated against approaches like BBO [44], SOS [45], SQP [44], CSO [46], OGSA [47], EP [48], and FA [49]. The proposed work utilized two three-ring designs; CCAA with 4-, 6-, 8-plus 8-, 10-, 12-, besides two cases well-thought-out for each model: CCAA without, and with the center component. ...
... However, because of the requirement of the cases, personal potentiality, and other assigned cases, the necessary time for each group to solve the same case was not the same. To find the best solution for the proposed work, DA and the Firefly Algorithm (FA) was utilized [47]. In [71], GWO, DA, and Moth-Flame Optimization (MFO) algorithms were assessed for optimizing the best sitting of the capacitor in several Radial Distribution Systems (RDSs). ...
... Nevertheless, the results from the references mentioned in the paper are another piece of evidence for the speed of convergence of the DA. Reference [47] utilized the DA and FA for optimizing the same problem. The results showed the high convergence of the DA. ...
Article
Full-text available
The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.
... Moreover, in this work, four different CCAA design cases used to study DA efficiency. Then, the results compared to the existing methods, such as BBO [48], SOS [49], SQP [48], CSO [50], OGSA [51], EP [52], and FA [53]. The proposed work utilized two three-ring designs, CCAA with 4-, 6-, 8-and 8-, 10-, 12-, and two cases considered for each model: CCAA without, and with centre element. ...
... Nevertheless, due to the case specification, personal capability, and other assigned cases, the teams needed to a spent different time to solve the same case. To find the optimal solution of the assignment problem, the proposed work used DA and firefly algorithm (FA) [51]. Two problems were examined to a uniform distribution. ...
... However, the results from the references mentioned above are another evidence for the convergence speed of the DA. Reference [51] utilized the FA and DA to optimize the same problem. The results showed that the DA converged earlier. ...
Preprint
p> Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems. </p
... Moreover, in this work, four different CCAA design cases used to study DA efficiency. Then, the results evaluated against the current approaches like BBO [38], SOS [39], SQP [38], CSO [40], OGSA [41], EP [42], and FA [43]. The proposed work utilized two three-ring designs; CCAA with 4-, 6-, 8-plus 8-, 10-, 12-, besides two cases well-thought-out for each model: CCAA without, and with the center component. ...
... However, because of the requirement of the cases, personal potentiality, and other assigned cases, the necessary time for each group to solve the same case was not the same. To find the best solution for the proposed work, DA and the firefly algorithm (FA) utilized [41]. (1, 90). ...
... Nevertheless, the results from the references mentioned above are another evidence for the speed of convergence of the DA. Reference [41] utilized the DA and FA for optimizing the same problem. The results showed the high convergence of the DA. ...
Preprint
p> Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems. </p
... Moreover, in this work, four different CCAA design cases used to study DA efficiency. Then, the results compared to the existing methods, such as BBO [48], SOS [49], SQP [48], CSO [50], OGSA [51], EP [52], and FA [53]. The proposed work utilized two three-ring designs, CCAA with 4-, 6-, 8-and 8-, 10-, 12-, and two cases considered for each model: CCAA without, and with centre element. ...
... Nevertheless, due to the case specification, personal capability, and other assigned cases, the teams needed to a spent different time to solve the same case. To find the optimal solution of the assignment problem, the proposed work used DA and firefly algorithm (FA) [51]. Two problems were examined to a uniform distribution. ...
... However, the results from the references mentioned above are another evidence for the convergence speed of the DA. Reference [51] utilized the FA and DA to optimize the same problem. The results showed that the DA converged earlier. ...
Preprint
p>Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems. Journal of Computational Design and Engineering, 2020. DOI: 10.1093/jcde/qwaa037 </p
... Moreover, in this work, four different CCAA design cases used to study DA efficiency. Then, the results compared to the existing methods, such as BBO [48], SOS [49], SQP [48], CSO [50], OGSA [51], EP [52], and FA [53]. The proposed work utilized two three-ring designs, CCAA with 4-, 6-, 8-and 8-, 10-, 12-, and two cases considered for each model: CCAA without, and with centre element. ...
... Nevertheless, due to the case specification, personal capability, and other assigned cases, the teams needed to a spent different time to solve the same case. To find the optimal solution of the assignment problem, the proposed work used DA and firefly algorithm (FA) [51]. Two problems were examined to a uniform distribution. ...
... However, the results from the references mentioned above are another evidence for the convergence speed of the DA. Reference [51] utilized the FA and DA to optimize the same problem. The results showed that the DA converged earlier. ...
Preprint
Full-text available
p>Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems. Journal of Computational Design and Engineering, 2020. DOI: 10.1093/jcde/qwaa037 </p
... These superior abilities make them suitable for electromagnetics problems where the optimisation of geometry and material parameters is undoubtedly a highly challenging and complex task. In the literature, nature-inspired techniques, such as symbiotic organisms search (SOS), biogeographybased optimisation (BBO), opposition-based gravitational search algorithm (OGSA), cat swarm optimisation (CSO), firefly algorithm (FA) and evolutionary programming (EP), have been successfully applied to the design of CCAAs and provide good results in terms of MSL performance (Dib, 2017;Dib & Sharaqa, 2015;Mandal, Ghoshal, & Bhattacharjee, 2010;Ram, Mandal, Kar, & Ghoshal, 2015a, 2015bSharaqa & Dib, 2014). In Dib and Sharaqa (2015), a sequential quadratic programming (SQP) method has also been used for CCAA design problem. ...
... DA is executed for 30 independent times and the best synthesis results of 30 runs are saved. The best results obtained by DA are compared with those obtained using the uniform array, EP (Mandal et al., 2010), FA (Sharaqa & Dib, 2014), CSO (Ram et al., 2015a), OGSA (Ram et al., 2015b), SQP (Dib & Sharaqa, 2015), BBO (Dib & Sharaqa, 2015) and SOS (Dib, 2017) and summarised in Tables 1-4 for CCAA having N 1 = 4, N 2 = 6, N 3 = 8 and N 1 = 8, N 2 = 10, N 3 = 12 elements with and without centre element. Comparative radiation patterns of the results given in Tables 1-4 are illustrated in Figures 3-6, respectively. ...
... The radiation patterns of CSO and OGSA are excluded from the plots given in Figures 3-6, because their corresponding radiation patterns and MSL values cannot be obtained using the excitation weights listed in Tables 1-4. Nevertheless, MSL values given in Ram et al. (2015aRam et al. ( , 2015b are added to Tables 1-4 to make a comparison of techniques in the recent literature. From Figures 3-6 and Tables 1-4, it is obvious that the best MSL results are obtained using DA. ...
Article
Due to the strong nonlinear 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 behaviors 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 (SOS, BBO, SQP, OGSA, CSO, FA, EP) for all design cases. DA can be a promising technique for electromagnetic problems.
... These side lobes can be suppressed by controlling the control parameters of the antenna arrays. [4][5][6][7][8][9] Different optimization techniques like particle swarm optimization (PSO), [10][11][12][13][14][15][16][17][18][19] genetic algorithm (GA), 20 block Krylov recycling algorithms, 21 hybrid technique, 22 Chichen swarm optimization, 23 using integral operator, 24 PSO, 25 bat algorithm, 26 differential evolution (DE) 21 are being used in the field of the antenna and electromagnetic as well as other fields of engineering and real-life application. ...
Article
Full-text available
Optimal design of antenna arrays to minimize the mutual coupling effects in the geometrical arrangements of the linear antenna array (LAA) and circular antenna array (CAA) is dealt with in this work. Two different cases are considered to reduce the effect of LAA and CAA: Case‐1 in which the current excitations of the antenna array are considered to get the optimal radiation pattern of two geometry called LAA and CAA and Case‐2 in which inter‐element spacing and current excitations are both optimized for LAA geometry. A cost function that involves the mutual coupling factor as an optimization factor is developed to reduce the side lobe level (SLL), which takes mutual coupling effects into consideration. Excitation values and inter‐elemental spacing are optimized using particle swarm optimization (PSO). In LAA, for 8‐, 12‐, 16‐element arrays, SLLs are reduced by −15.52, −16.71, and −17.78 dB in Case‐1. For the same sets of element arrays, SLLs were reduced by −17.35, −19.71, and −20.26 dB in Case‐2. In CAA, the current excitations of the antenna array are optimized. For 8‐, 12‐, and 16‐ element arrays, SLLs are reduced to −7.405, −10.52, and −9.43 dB, respectively. The arrays coded with the help of MATLAB based computation and the results obtained by MATLAB are validated by using CST. Optimal design of antenna arrays to minimize the mutual coupling effects in the geometrical arrangements of the linear antenna array (LAA) and circular antenna array (CAA) are dealt with in this work. Two different cases are considered to reduce the effect of LAA and CAA: Case‐1 in which the current excitations of the antenna array are considered to get the optimal radiation pattern of two geometry called LAA and CAA and Case‐2 in which inter‐element spacing and current excitations are both optimized for LAA geometry.
... Circular and concentric circular array geometries are presented in [14][15][16][17][18]. To overcome this problem, the evolutionary algorithm is applied to work out the composite non-linear and non-differentiable problems [19][20][21][22][23][24][25][26]. There are many evolutionary algorithms which are used for different fields of engineering [27][28][29][30][31][32][33][34][35] and the optimisation of antenna problem [36][37][38][39][40]. Development of element level antenna like the dielectric resonator antenna also has been reported in [41]. ...
... Hence, the CF minimization signifies the maximum reduction of SLL as well as FNBW value. A lot of researchs have already been done for the optimal radiation pattern synthesis of CAA using different optimization techniques [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. But, in this paper, GWO algorithm is employed to optimize the current excitation weights and inter-element spacing among the array elements of the CDAA for the maximum reduction of CF value. ...
... The key feature of a circular ring array is the cylindrical symmetry of its radiation pattern and of compact structure, thus finding considerable interest in various applications including radio direction finding [1], radar [13], communication [14], and electronic countermeasures, navigation and imaging [15]. Different evolutionary algorithms, genetic algorithm (GA) [16][17][18] and particle swarm optimization (PSO) [19][20][21][22], differential evolution (DE) [6], [20][21] have been employed for low side lobe array pattern synthesis of concentric circular antenna arrays (CCAA) [22][23][24][25][26][27][28][29][30][31][32][33][34]. Not only in the field of electromagnetic but also there are various fields of engineering which use the application of evolutionary algorithms [35][36][37][38][39][40][41][42]. ...
Article
Full-text available
In this paper, Differential Evolution with Wavelet Mutation (DEWM) is applied to the radiation pattern synthesis of circular geometries of antenna array. Two circular geometries have been considered: (a) Time-Modulated Half-Symmetric Circular Antenna Array (TMHSCAA); (b) 9-ring Time Modulated Concentric Circular Antenna Array (TMCCAA). DEWM algorithm is applied to show the performance improvement of the optimal designs of TMHSCAA and TMCCAA. While doing so, various other stochastic algorithms, such as Real coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), are used for the sake of comparison to establish the superiority of DEWM. For TMHSCAA, elements are symmetrical around the vertical axis; therefore, the number of parameters to be optimized is reduced with two control parameters, such as switching excitation phase of each element. For TMCCAA, two proportional case studies, Case 1 and Case 2, are investigated with different variable parameters. The simulation outcomes show the supremacy of DEWM to be a plausible claimant for scheming the best TMHSCAA and TMCCAA. The simulation tests have also been performed with 20- and 36-element TMHSCAAs and 9-ring TMCCAA.
Article
This paper proposes an algorithm called cat swarm optimization (CSO) for the optimal design of non-uniform single-ring circular antenna array (CAA) and non-uniform three-ring concentric circular antenna array (CCAA). The algorithm is used to determine an optimum set of current excitation weights and antenna inter-element separations for CAA of 8, 10 and 12 elements and optimal current excitation weights for CCAA, respectively, which provide radiation pattern with maximum reduction of side lobe level (SLL). Two 3-ring CCAAs, one having the set of 4-, 6-, 8-, elements and the other having 8-, 10-, 12- elements, with and without centre element are considered. Simulation results show a considerable improvement of SLL and some restricted improvement of 3-dB beamwidth with respect to the corresponding uniform cases of both the types of antenna array and the corresponding results of some recent literature reported in this paper.
Conference Paper
In this paper, a new evolutionary optimization algorithm named gravitational search algorithm with wavelet mutation (GSAWM) is adopted for optimal design of hyper beam pattern of linear antenna arrays. Hyper beam is derived from sum and difference beam patterns associated with hyper beam exponent parameter for the array. In GSAWM, particles are considered as objects and their performances are measured by their masses. All these objects attract each other by gravity forces, and these forces produce global movements of all objects towards the objects with heavier masses. GSAWM guarantees the exploitation step of the algorithm and it is apparently free from premature convergence. Extensive simulation results justify superior optimization capability of GSAWM over the aforementioned optimization techniques. By optimization of current excitation weights and uniform inter-element spacing, GSAWM achieves optimized hyper beam with much greater reduction in side lobe level (SLL), improved directivity and much more improved first null beam width (FNBW), keeping the same value of hyper beam exponent. The whole simulation experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.
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In this paper the authors propose the optimal radiation pattern synthesis of time modulated linear isotropic antenna array with improved directivity and maximum reduction of side lobe level (SLL) and minimum side band levels (SBL) at its harmonics based on evolutionary algorithms applied individually. Evolutionary algorithms are real coded genetic algorithm (RGA), particle swarm optimisation (PSO), novel particle swarm optimisation (NPSO), and the fourth dimension parameter as time. The same array radiates at various harmonic frequencies. The first two harmonic frequencies have been considered in this work. The objective function judiciously chosen enables comparative performance evaluation of the case studies. The numerical results show Case-4 outperforms Case-1, Case-2, and Case-3 with respect to better side lobe level and more improved directivity. Again, the performance of NPSOWM is the best among RGA, PSO, and NPSO for all the case studies. The numerical results also show power radiated by any harmonic frequency is very less as compared to the power radiated at centre frequency called fundamental frequency. It has also been observed that as the harmonic frequency increases, sometimes SBL increases as compared to SLL but powers radiated by the antenna at its harmonic frequencies decrease. As per authors knowledge there is no contribution by any other previous researcher regarding numerical computation of powers radiated at fundamental frequency and its two harmonic frequencies, dynamic efficiency and directivity for time modulated linear antenna arrays.
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Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems. Success achieved for those evolutionary optimization techniques are due to simplicity and flexibility of algorithm structures. In this paper, optimal set of filter coefficients are searched by the evolutionary optimization technique called Opposition-based Differential Evolution (ODE) for solving infinite impulse response (IIR) system identification problem. Opposition-based numbering concept is embedded into the primary foundation of Differential Evolution (DE) technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution. The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle. With this competent population, faster convergence speed and better solution quality are achieved. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation, crossover and selection adopted in the basic DE technique. When tested against standard benchmark examples, for same order and reduced order models, the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed.