Hybrid multiobjective optimization using modified cuckoo search algorithm in linear array synthesis

In this study, we postulated the hybridization of modified cuckoo search (MCS) with two evolutionary algorithms (EAs), which were particle swarm optimization (PSO) and genetic algorithms (GA) in weighted-sum multiobjective optimization towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL) and/or nulls mitigation. The newly nature-inspired MCS algorithm was principally based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. Through the integration with the Roulette wheel selection operator and the inertia weight controlling the exploration of host nest position (solution), the proposed hybrid MCS-based approach optimized simultaneously three element excitation components including locations, amplitudes, and phases, respectively. The optimal solutions obtained were then analyzed for relative performance comparisons against the conventional, original CS, individual MCS, and hybrid GAPSO-based linear arrays.