Meta-heuristic optimization algorithms for simultaneous optimization of sidelobe level and directivity of uniformly excited concentric ring array antennas

2019 ◽  
Vol 12 (2) ◽  
pp. 183-192
Author(s):  
Kailash Pati Dutta ◽  
G. K. Mahanti
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Beam Width ◽  
Simultaneous Optimization ◽  
Heuristic Optimization ◽  
The Novel ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Array Antennas ◽  
Symbiotic Organism Search

AbstractThis paper proposes the novel application of three meta-heuristic optimization algorithms namely crow search algorithm, moth flame optimization, and symbiotic organism search algorithm for the synthesis of uniformly excited multiple concentric ring array antennas. The objective of this work is to minimize the sidelobe level (SLL) and maximize the peak directivity simultaneously. Three different cases are demonstrated separately with various constraints such as optimal inter-element spacing and/or optimal ring radii. Comparative study of the algorithms using common parameters such as SLL, directivity, first null beam width, best cost, and run time has been reported. Investigation results prove the superiority of case 3 over other cases in terms of directivity and SLL. This work demonstrates the potential of these algorithms.

Gravitational search algorithm for synthesis of selectively thinned concentric ring array antenna with minimum sidelobe level and with fixed and variable first null beamwidth

2014 ◽  
Vol 7 (6) ◽  
pp. 775-781 ◽  
Author(s):  
Anirban Chatterjee ◽  
Gautam Kumar Mahanti ◽  
Narendra Nath Pathak
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Synthesis Method ◽  
Computational Time ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Pattern Synthesis ◽  
First Case ◽  
Gravitational Search ◽  
First Null Beamwidth

Thinning a large concentric ring array by an evolutionary algorithm needs to handle a large amount of variables. The computational time to find out the optimum elements set increases with the increase of array size. Moreover, thinning significantly reduces the directivity of the array. In this paper, the authors propose a pattern synthesis method to reduce the peak sidelobe level (peak SLL) while keeping first null beamwidth (FNBW) of the array fixed by thinning the outermost rings of the array based on Gravitational Search Algorithm (GSA). Two different cases have been studied. In the first case only the outermost ring of the array is thinned and in the second case the two outermost rings are thinned. The FNBW of the optimized array is kept equal to or less than that of a fully populated, uniformly excited and 0.5 λ spaced concentric ring array of same number of elements and rings. The directivity of the optimized array for the above two cases are compared with an array optimized by thinning all the rings, while keeping the design criteria same as the above two cases. The optimized array by thinning the outermost rings gives higher directivity over the optimized array by thinning all the rings. Time required for computing the optimum elements state for the above two cases using GSA are shown lesser compared to the optimized array by thinning all the rings using the same algorithm. The peak SLL and the FNBW of the optimized array for the above two cases are also compared with the optimized array by thinning all the rings.

scholarly journals Algoritma Improved Symbiotic Organism Search (I-SOS) sebagai Pendukung Keputusan dalam Penentuan Dosen Pengampu Matakuliah

2020 ◽  
Vol 1 (4) ◽  
pp. 279-286
Author(s):  
Zulkarnaen ◽  
Herman Budianto ◽  
Hendrawan Armanto
Keyword(s):  
Search Algorithm ◽  
Heuristic Optimization ◽  
Behavioral Interactions ◽  
Teaching Load ◽  
Metaheuristic Methods ◽  
Previous Algorithm ◽  
Conventional Methods ◽  
Symbiotic Organism Search ◽  
The Universe

This study discusses the allocation of lecturers to apply courses by applying the concept of heuristic optimization using the Improved Symbiotic Organism Search (I-SOS) algorithm. The I-SOS Algorithm is a development of the previous algorithm, the Symbiotic Organism Search Algorithm, which is one of the latest metaheuristic methods inspired by the behavioral interactions seen between organisms in the universe. Optimizing the allocation of lecturers is very influential on the preparation of class schedules because scheduling a good course must be able to meet the needs of the parties directly related to the scheduling process. Conventional methods used today often cause problems in terms of efficiency and accuracy, imbalance of teaching load and inaccuracy in lecturer assignments requiring lecturers to take courses not in accordance with their interests and expertise.

scholarly journals Economic and emission dispatch using cuckoo search algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 3384
Author(s):  
Rachid Habachi ◽  
Abdellah Boulal ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Brood Parasitism ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Cuckoo Search Algorithm ◽  
Heuristic Optimization ◽  
Generator System ◽  
Emission Dispatch

<p class="Default">The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes the use of Cuckoo Search Algorithm (CSA) for solving the economic and Emission dispatch. The effectiveness of the proposed approach has been tested on 3 generator system. CSA is a new meta-heuristic optimization method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species.The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature</p>

scholarly journals Synthesis of Linear Array Antenna for Required Range of Beam Width Using Optimization Algorithms

2018 ◽  
Vol 7 (3.31) ◽  
pp. 16
Author(s):  
N Venkateswara rao ◽  
G Challa Ram
Keyword(s):  
Linear Array ◽  
Optimization Algorithms ◽  
Differential Evolution Algorithm ◽  
Beam Width ◽  
Side Lobe ◽  
Array Antenna ◽  
Simultaneous Optimization ◽  
Side Lobe Level ◽  
Wide Range ◽  
Evolution Algorithm

In an application like radar there is a need for a wide range of Beam widths depending on whether the radar is operating in search mode or tracking mode. Wide range of beam widths can be achieved by using optimization algorithms like Biogeography-based optimization (BBO) and Differential Evolution Algorithm (DE). The desired beam width should be achieved without any significant increase in the side lobe level (SLL). This can be done by optimizing both SLL and FNBW simultaneously. Synthesis of linear array antenna for a fixed range of beam width is obtained by using the proposed methodology. The results for simultaneous optimization of FNBW and SLL using BBO and DE algorithms are compared.  

Meta-heuristic optimization algorithms for synthesis of reconfigurable hexagonal array antenna in two principle vertical planes

2021 ◽  
pp. 1-8
Author(s):  
Bitan Misra ◽  
Gautam Kumar Mahanti
Keyword(s):  
Optimization Algorithms ◽  
Amplitude Distribution ◽  
Excitation Amplitude ◽  
Heuristic Optimization ◽  
Simulation Based ◽  
Optimal Current ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organism Search ◽  
Flat Top Beam

Abstract This study illustrates the dynamical reconfiguration of a concentric hexagonal antenna array radiation to generate a pencil beam and flat-top beam simultaneously by electronic control in two principle vertical planes under consideration. Both the beams share a common normalized optimal current excitation amplitude distribution while the optimal sets of phase excitation coefficients are varied radically across the hexagons to generate a flat-top beam. The proposed approach is able to solve the underlying multi-objective problem and flexible enough to the efficient implementation of additional design constraints in the considered φ-planes. In this paper, a set of simulation-based examples are presented in an integrated way. The outcomes validate the effectiveness of the stated optimization using meta-heuristic optimization algorithms (teaching–learning-based optimization, symbiotic organism search, multi-verse optimization) to reach the solution globally and prove actual relevance to the concerned applications.

scholarly journals Performance comparison of various probability gate assisted binary lightning search algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 299 ◽  
Author(s):  
Md Mainul Islam ◽  
Hussain Shareef ◽  
Mahmood Nagrial ◽  
Jamal Rizk ◽  
Ali Hellany ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Search Space ◽  
Performance Comparison ◽  
Optimization Techniques ◽  
Natural Phenomenon ◽  
Heuristic Optimization ◽  
Quantum Bit

<div style="’text-align: justify;">Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm(LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm(BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA.</div>

scholarly journals Crow Search Algorithm (CSA)

2019 ◽  
Vol 8 (3S2) ◽  
pp. 371-375
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Metaheuristic Algorithms ◽  
Heuristic Optimization ◽  
Keen Interest ◽  
Diverse Application ◽  
Complex Building

Researchers’ are taking keen interest in Optimization algorithms due to their heuristic and meta-heuristic nature. Heuristic algorithms find the arrangement by utilizing the experimentation strategy. Then again, meta-heuristic algorithms discover the response at a more elevated tier. Several nature-based metaheuristic algorithms are easily accessible. Askarzadeh has introduced the Crow search algorithm and stated that it is meta-heuristic optimization algorithm. The astute conduct of the crow moves CSA. Crows are keen on putting away the abundance nourishment at concealing spots and recuperating it at whatever point it is needed. CSA's previous outcomes show that it can unravel different complex building related optimization issues. There are six compelled building plan issues, and CSA is utilized to upgrade these issues. This paper focuses on a far-reaching investigation of CSA in the diverse application is given with the examination just as the exhibitions of the CSA in the different structure is talked about.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

2021 ◽  
Vol 35 (4) ◽  
pp. 1149-1166
Author(s):  
Hossien Riahi-Madvar ◽  
Majid Dehghani ◽  
Rasoul Memarzadeh ◽  
Bahram Gharabaghi
Keyword(s):  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
River Flows ◽  
Long Term Forecasting
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