Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this mind, quantum-inspired evolutionary algorithms' ability to improve population quality solutions, work investigates performance genetic algorithm (QIGA), has been adapted with RCPSPs. proposed QIGA possesses same structure as classical algorithms, but initial updated populations are implemented using quantum gates superposition, bearing mind adaptation such operators fit discrete problems. This aims solve RCPSPs investigate influence various parameters algorithm, size, different options for re-combination use QIGA. well-known PSPLIB benchmark instances J30, J60 J120 activities used test effectiveness It apparent from results that mutation, crossover representation superposition enhances diversity. also outperform other algorithms.