Hybrid differential evolution and enhanced particle swarm optimisation technique for design of reconfigurable phased antenna arrays

Abstract

This study introduces a new design method for reconfigurable phased arrays using hybrid differential evolution (DE) and enhanced particle swarm optimisation (EPSO) technique. The proposed technique combines DE and enhanced version of standard PSO with improved mechanism that updates velocities and global best solution. In the hybrid algorithm, DE and EPSO are executed in parallel with frequent information sharing to enhance the newly generated population. To demonstrate the effectiveness of the proposed algorithm over each separate algorithm, examples for designing reconfigurable linear and circular antenna arrays with prescribed null directions are presented. Null steering is achieved by position perturbation of array elements in arbitrary directions with minimum sidelobe level change constraint. Another objective is to minimise the number of mobilised elements by introducing elements selection criteria. Simulation results show that the global search ability of the proposed algorithm is improved when compared with DE and EPSO separately.

Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization

“…Based on a greedy selection strategy, an individual is generated from the target vector and trial vectors, and is entered to the next generation. The procedures for implementing a DE algorithm can be summarized as the following steps [11] :…”

Mamdani and Takagi-Sugeno fuzzy classifier accuracy improvement using enhanced particle swarm optimization

“…For classical PSO, each individual of the population adjusts its trajectory toward its own previous best position, and toward the previous best position attained by any member of its topological neighborhood. Enhanced practical swarm optimization (EPSO) algorithm [34,35] is similar to classical PSO with improved global search ability. This is accomplished by introduce an updating formula for global best particle position and adding two new terms in the velocity updating formula of classical PSO.…”

“…In this paper, two fuzzy classifiers are proposed and optimized with enhanced version of particle swarm optimization (EPSO) [34,35]. The first classifier is based on Mamdani fuzzy inference system and the second one is based on Takagi-Sugeno fuzzy inference system.…”

Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

“…In such a hybrid algorithm, the weaknesses associated with each algorithm are negated by the strengths of others, while common strengths are cumulated. Hybrid algorithms can be developed by combining metaheuristic algorithms with one or more existing algorithms, such as dynamic programming [TSE et al 2007], constraint programming [MAYER 2008;PRESTWICH 2002], tree search method [BLUM 2005;ROTHBERG 2007] or even with another metaheuristic algorithm [ELRAGAL et al 2011;OLDENHUIS 2010;ZHANG et al 2009].…”