摘要
针对复杂条件下多水下机器人系统(AUVs)任务分配过程中各个节点负载不均衡问题,提出混沌优化QPSO算法.以整个量子粒子群搜索到的当前最优位置为基础,在混沌QPSO算法中加入混沌因子,产生混沌序列.利用混沌优化中混沌搜索、搜索遍历性等具有类似协同学习操作的功能,用混沌序列中的最优位置的粒子替代当前量子粒子群中的位置,使得近似最优解脱离局部最优,获得真正的全局最优.通过实验证明:混沌优化QPSO算法在多AUVs任务分配中,提高了任务分配的精度和优化效率,使任务分配达到全局最优值.
Focusing on each node load imbalance problem of multiple autonomous underwater vehicles system(MAUVs)under the complicated conditions in the process of task allocation,chaos optimization QPSO algorithm was proposed.It was based on the searched current optimal position of quantum particle swarm,chaos factor was added to chaos QPSO algorithm and generated chaotic sequence.Using chaotic searching in chaos optimization,searching ergodicity property etc,which have similar function of collaborative learning operation,the particles of optimal location in the chaotic sequence replacing the current position of quantum particle swarm,making the approximate optimal breaking away from local optimum and getting the real global optimum.The experiment results show that chaos optimization QPSO algorithm in the process of multiple AUVs' task allocation,improving the accuracy of the task allocation and optimization efficiency,bring task allocation up to global optimal value.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第S1期424-427,共4页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60975071
61100005)
教育部科学研究项目(13YJA790123)
关键词
混沌优化
量子粒子群
混沌量子粒子群
多水下机器人系统
任务分配
chaos optimization
quantum-behaved particle swarm
chaotic quantum-behaved particle swarm
multiple autonomous underwater vehicle system
task allocation
