Bloch quantum-behaved Pigeon-inspired optimization for continuous optimization problems

In this paper, a novel hybrid Pigeon Inspired Optimization (PIO) and quantum theory is proposed for solving continuous optimization problem. Which is called Bloch Quantum-behaved Pigeon-Inspired Optimization (BQPIO for abbreviation). Quantum theory is adopted to increase the local search capacity as well as the randomness of the position. As a consequence, the improved BQPIO can avoid the premature convergence problem and find the optimal value correctly when solving multimodal problems. An empirical study was carried out to evaluate the performance of the proposed algorithm, which is compared with Particle Swarm Optimization (PSO), basic PIO, and Quantum-behaved Particle Swarm Optimization (QPSO). The comparative results demonstrate that our proposed BQPIO approach is more feasible and effective in solving complex continuous optimization problems compared with other swarm algorithm.