A quantum-inspired gravitational search algorithm for binary encoded optimization problems

In this paper, a novel population based metaheuristic search algorithm by combination of gravitational search algorithm (GSA) and quantum computing (QC), called a Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA), is proposed. BQIGSA uses the principles of QC such as quantum bit, superposition and a modified rotation Q-gates strategy together with the main structure of GSA to present a robust optimization tool to solve binary encoded problems. To evaluate the effectiveness of the BQIGSA several experiments are carried out on the combinatorial 0–1 knapsack problems, Max-ones and Royal-Road functions. The results obtained are compared with those of other algorithms including Binary Gravitational Search Algorithm (BGSA), Conventional Genetic Algorithm (CGA), binary particle swarm optimization (BPSO), a modified version of BPSO (MBPSO), a new version of binary differential evolution (NBDE), a quantum-inspired particle swarm optimization (QIPSO), and three well-known quantum-inspired evolutionary algorithms (QIEAs). The comparison reveals that the BQIGSA has merit in the field of optimization.