Nonuniformly Spaced Linear Antenna Array Design Using Firefly Algorithm

Abstract

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.

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

“…Additionally, the metaheuristic algorithms are capable of escaping from local minima. Genetic algorithm (GA) [7-12, 20, 23, 25, 39], tabu search algorithm (TSA) [23,30], modified touring ant colony algorithm (MTACO) [13], particle swarm optimization (PSO) [15,30,33], bees algorithm (BA) [16,29], bacterial foraging algorithm (BFA) [19,24], clonal selection algorithm (CLONALG) [21], plant growth simulation algorithm (PGSA) [26], differential evolution (DE) algorithm [14,18,27], biogeography based optimization (BBO) [28], multiobjective DE (MODE) [30], memetic algorithm (MA) [17,23,30], nondominated sorting GA-2 (NSGA-2) [30], decomposition with DE (MOEA/D-DE) [30], comprehensive learning PSO (CLPSO) [31], seeker optimization algorithm (SOA) [32], invasive weed optimization (IWO) algorithm [34], harmony search algorithm (HSA) [35], firefly algorithm (FA) [36,38], cuckoo search (CS) algorithm [37,42], differential search algorithm (DSA) [40], cat swarm optimization (CSO) [41], and mean variance mapping optimization (MVMO) [43] can be given as the examples of these metaheuristic algorithms used for pattern nulling.…”

Neural modeling of Gene Regulatory Network using Firefly algorithm

“…Firefly ptimize continuous function. It PSO [23].It deals efficiently with mization problem [22]. The ulation or a swarm of fireflies.…”

Optimal groundwater remediation design of pump and treat systems via a simulation–optimization approach and firefly algorithm

“…For example, it has superior performance in problems with large noise rates compared to the Bees algorithm and so it is more appropriate for exploiting a search space (Chaiead, Aungkulanon, and Luangpaiboon 2011). Moreover, it results in superior convergence and better results compared with PSO (Basu and Mahanti 2011;Chaiead, Aungkulanon, and Luangpaiboon 2011;Gandomi,Yang, andAlavi 2011;Gandomi,Yang, Talatahari, andAlavi 2012;Parpinelli 2011;Zaman and AbdulMatin 2012), quantum particle swarm optimization (QPSO) (Horng and Liou 2011), ABC (Basu and Mahanti 2011), SA (Basu and Mahanti 2011), network management system (NMS) (Basu and Mahanti 2011), harmony search (HS) (Miguel and Miguel 2012) and exhaustive research (Horng and Liou 2011). It has also been shown to be superior to GAs (Gandomi, Yang, and Alavi 2011;Gandomi et al 2012) because fireflies aggregate more closely around each optimum without 'jumping around'.…”