Adaptive pattern nulling design of linear array antenna by phase‐only perturbations using memetic algorithms

Abstract

SUMMARYIn this paper, the pattern nulling of a linear array for interference cancellation is derived by phase-only perturbations using memetic algorithms (MAs). The MAs uses improvement procedures which is obtained by incorporating local search into the genetic algorithms. It is proposed to improve the search ability of genetic algorithms. MA is a kind of an improved type of the traditional genetic algorithms. By using local search procedure, it can avoid the shortcoming of the traditional genetic algorithms, whose termination criteria are set up by using the trial and error method. The MA is applied to find the pattern nulling of the proposed adaptive antenna. This design for radiation pattern nulling of an adaptive antenna can suppress interference by placing a null at the direction of the interfering source, i.e. to increase the signal to interference ratio. This proposed method is that an innovative adaptive antenna optimization technique is also able to solve the multipath problem which exists in practical wireless communication systems. Two examples are provided to justify the proposed phase-only perturbations approach based on MAs.Computer simulation results are given to demonstrate the effectiveness of the proposed method.

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.…”

Modeling Polarization of a DMFC System via Neural Network with Immune-Based Particle Swarm Optimization

“…With reference to the research of Meyers and Newman (2002), the back propagation neural network (BPNN) model with one hidden layer is shown in Figure 3. The input x i of the NN model is expressed as Figure 4 (Kong, Hodgson, and Collinson 1998;Cantu 2000;Akis et al 2001;Hippert, Pedreira, and Souza 2001;Alba and Tomassini 2002;Fogel 2005;Yu, Chen, and Mu 2005;Zweiri, Seneviratne, and Althoefer 2005;Facão, Varga, and Oliveira 2006;Hsu et al 2007;Mohanraj, Jayaraj, and Muraleedharan 2008). During the learning procedure, the equation for updating the weights in momentum learning is…”

Robust Stability Analysis of Discrete Time System via an Optimization Approach

“…For many problems there exists a well developed, efficient search strategy for local improvement, e.g., hill-climbers for optimization. These local search strategies compliment the global search strategy of the genetic algorithms, yielding a more efficient overall search strategy [13][14][15][16].…”