【摘要】 针对变色龙群算法(CSA)求解精度低、稳定性弱和易陷入局部最优等缺陷，提出种群活跃与混沌变螺旋策略变色龙群算法(ICSA)。在CSA初始化中，引入偶对称无限折叠混沌序列，初始化中能更好遍历搜索空间，提升多样性；考虑变色龙眼睛转动寻食的盲目性，引入变螺旋指引策略缩小转动的目标，同时利用自适应惯性权重平衡算法的搜索开发，增强算法搜索能力；引入黎曼流形量子学习策略，在后期提升种群活跃度使算法跳出局部最优解，提升开发能力。利用CEC函数集测试算法的有效性、可靠性、算法性能和时间复杂度分析；以无源时差定位(TDOA)为场景验证在无源定位中的求解性能。多项实验表明，ICSA算法寻优精度和稳定性均得以有效改善、在TDOA定位中准确率得到有效提升。更多还原

【Abstract】 Aiming at the shortcomings of chameleon swarm algorithm(CSA),such as low accuracy, weak stability and easy to fall into local optimization, a chameleon swarm algorithm based on active population and chaotic variable helix strategy(ICSA)is proposed. In CSA initialization, even symmetric infinite folding chaotic sequence is introduced, which can better traverse the search space and improve the diversity. Considering the blindness of the chameleon’s eye rotation for food, the variable spiral guidance strategy is introduced to reduce the rotation target. At the same time, the search development function of the adaptive inertia weight balance algorithm is used to enhance the search ability of the algorithm. The Riemannian manifold quantum learning strategy is introduced to improve the population activity in the later stage, make the algorithm jump out of the local optimal solution and improve the development ability. CEC function set is used to test the effectiveness, reliability, performance and time complexity of the algorithm. Time difference of arrival(TDOA)taken as a scenario, the solution performance in passive location is verified. Many experiments show that the optimization accuracy and stability of ICSA can be effectively improved, and the accuracy in TDOA positioning can be effectively improved.更多还原