Linear antenna array synthesis using the enhanced and hybrid cuckoo search metaheuristic algorithms

The antenna geometry synthesis plays an important role to determine the physical layout of the antenna array, which produces the radiation pattern closest to the actual desired pattern. The synthesis can be realized by defining the location of antenna array elements, and by choosing suitable excitation of amplitude, and excitation phase applied on the antenna array elements. Many synthesis techniques are done through suppressing the side lobe level (SLL) and/or mitigating prescribed nulls while simultaneously maintaining or improving the major lobe radiation intensity. Studies show that some conventional analytical, numerical, and modern evolutionary algorithm (EA) or evolutionary computation (EC) techniques have certain limitations in antenna array geometry synthesis. This includes beamwidth expanding and directivity saturation in amplitude tapering, exhaustive checking impairment in analytical method, disparity predicament between local and global search accelerators in particle swarm optimization (PSO), and drawbacks of crossover and mutation operators in genetic algorithm (GA). This thesis presents the sequential development of enhanced and hybrid versions of cuckoo search (CS) metaheuristic algorithm as an alternative of EA/EC technique for symmetric linear antenna array synthesis. Firstly, the proposal of the modified CS (MCS) algorithm through the integration with the Roulette wheel selection operator, dynamic inertia weight, and dynamic discovery rate controlling the best solutions exploration for a single objective (SO) optimization. Secondly, there is the hybridization of MCS with PSO (MCSPSO), and MCS with GA (MCSGA) in both SO and weighted−sum multiobjective (MO) approaches. Thirdly, the proposed amalgamation of MCS with strength Pareto evolutionary algorithm (MCSSPEA), hill climbing (HC) stochastic method within MCSSPEA algorithm (MCSHCSPEA), and PSO within MCSSPEA algorithm (MCSPSOSPEA) equipped with distance expansion formulae to reduce local trap problem. These newly techniques are specifically for Pareto MO optimization to find non−dominated solutions including element location, excitation amplitude, and excitation phase. All the tested algorithms development, source code writing, and results execution are performed using MATLAB scientific software. The optimal solutions are then compared against corresponding counterparts. Based on simulation results, the proposed MCSPSO outperforms other SO and weighted−sum MO algorithms whereas the proposed MCSPSOSPEA algorithm surpasses other tested Pareto MO algorithms in SLL suppression and/or nulls mitigation whilst achieving a high linear antenna directivity, and small half−power beamwidth (HPBW), respectively.