Pramanik, Amit KumarMahalat, Mahabub HasanPal, JayantaAhmadpour, Seyed-SajadSen, Bibhash2023-10-192023-10-192023100920-85421573-0484https://doi.org/10.1007/s11227-022-04757-0https://hdl.handle.net/20.500.12469/5503Quantum-dot cellular automata (QCA) is a field coupling nano-technology that has drawn significant attention for its low power consumption, low area overhead, and achieving a high speed over the CMOS technology. Majority Voter (MV) and QCA Inverter (INV) are the primitive logic in QCA for implementing any QCA circuit. The performance and cost of a QCA circuit directly depend on the number of QCA primitives and their interconnections. Their optimization plays a crucial role in optimizing the QCA logic circuit synthesis. None of the previous works considered elitism in GA, all the optimization objectives (MV, INV and Level), and the redundancy elimination approach. These profound issues lead us to propose a new methodology based on Genetic algorithm (GA) for the cost-effective synthesis of the QCA circuit of the multi-output boolean functions with an arbitrary number of inputs. The proposed method reduces the delay and gate count, where the worst-case delay is minimized in terms of the level. This methodology adapts elitism to preserve the best solutions throughout the intermediate generations. Here, MV, INV, and levels are optimized according to their relative cost factor in a QCA circuit. Moreover, new methodologies are proposed to create the initial population, maintain the variations, and eliminate redundant gates. Simulation results endorse the superiority of the proposed method.eninfo:eu-repo/semantics/closedAccessMultiobjective OptimizationGenetic algorithmDesignCircuit synthesisQuantum-dot cellular automataMultiobjective OptimizationCircuit optimizationDesignElitismArticle38503877479WOS:00085329440000110.1007/s11227-022-04757-02-s2.0-85138031924N/AQ2