A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS still suffers from deficiency not being able to handle high-dimensional variables. A quantum-based optimization (QBSO) proposed herein address this deficiency. In order maintain population diversity and improve avoidance falling into local optimal solutions, a novel quantum representation-based position updating strategy designed. current best solution regarded as linear superposition two probabilistic states: positive deceptive. An increase in or reset probability state performed through rotation gate global ability. Finally, variable step adopted speed up ability convergence. QBSO verified against several algorithms, results show that has satisfactory performance at very small size.