Abstract: In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO).

Key words: Quantum firefly algorithm, Swarm intelligence, Global optimization, Chaotic map, Test functions