A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem

• Quantum theory is introduced to artificial bee colony algorithm (ABC) to increase population diversity.
• A chaotic local search operator is used to enhance local search ability of ABC.
• Quantum inspired chaotic ABC method (QCABC) is proposed to solve optimal power flow.
• The feasibility and effectiveness of the proposed QCABC is verified by examples.

This paper proposes a new artificial bee colony algorithm with quantum theory and the chaotic local search strategy (QCABC), and uses it to solve the optimal power flow (OPF) problem. Under the quantum computing theory, the QCABC algorithm encodes each individual with quantum bits to form a corresponding quantum bit string. By determining each quantum bits value, we can get the value of the individual. After the scout bee stage of the artificial bee colony algorithm, we begin the chaotic local search in the vicinity of the best individual found so far. Finally, the quantum rotation gate is used to process each quantum bit so that all individuals can update toward the direction of the best individual. The QCABC algorithm is carried out to deal with the OPF problem in the IEEE 30-bus and IEEE 118-bus standard test systems. The results of the QCABC algorithm are compared with other algorithms (artificial bee colony algorithm, genetic algorithm, particle swarm optimization algorithm). The comparison shows that the QCABC algorithm can effectively solve the OPF problem and it can get the better optimal results than those of other algorithms.