An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missi...An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
灰狼优化算法(Grey Wolf Optimizer,GWO)和人工蜂群算法(Artificial Bee Colony,ABC)是两种流行且高效的群智能优化算法. GWO具有局部搜索能力强等优势,但存在全局搜索能力弱等缺陷;而ABC具有全局搜索能力强等优点,但存在收敛速度慢等不...灰狼优化算法(Grey Wolf Optimizer,GWO)和人工蜂群算法(Artificial Bee Colony,ABC)是两种流行且高效的群智能优化算法. GWO具有局部搜索能力强等优势,但存在全局搜索能力弱等缺陷;而ABC具有全局搜索能力强等优点,但存在收敛速度慢等不足.为实现二者优势互补,提出了一种GWO与ABC的混合算法(Hybrid GWO with ABC,HGWOA).首先,使用静态贪心算法替代ABC雇佣蜂阶段中的动态贪心算法来强化探索能力,同时为弥补其收敛速度降低的不足,提出一种新型的搜索蜜源方式;然后,去掉影响收敛速度的侦查蜂阶段,在雇佣蜂阶段再添加反向学习策略,以避免搜索陷入局部最优;最后,为了平衡以上雇佣蜂阶段的探索能力,在观察蜂阶段,自适应融合GWO,以便增强开采能力和提高优化效率.大量的函数优化和聚类优化的实验结果表明,与state-of-the-art方法相比,HGWOA具有更好的优化性能及更强的普适性,且能更好地解决聚类优化问题.展开更多
为了提高求解多目标优化问题的Pareto解集的效率,建立了适用于多维、多目标优化问题的多目标蚁群算法(Multi-objective Ant Colony Algorithm,简称MACA)。该算法首先修正了蚁群算法的信息素更新机制和转移概率,然后改进了蚂蚁的行进策略...为了提高求解多目标优化问题的Pareto解集的效率,建立了适用于多维、多目标优化问题的多目标蚁群算法(Multi-objective Ant Colony Algorithm,简称MACA)。该算法首先修正了蚁群算法的信息素更新机制和转移概率,然后改进了蚂蚁的行进策略,即提出了依概率选择搜索策略。最后,应用该算法对某型号固液混合火箭发动机系统进行了优化设计。计算结果表明,多目标蚁群算法获得的Pareto解集分布均匀、散布范围广,可以有效解决多目标优化问题,能为决策者进行目标权衡提供充分依据。展开更多
基金supported by the National Aviation Science Foundation of China(20090196002)
文摘An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
文摘灰狼优化算法(Grey Wolf Optimizer,GWO)和人工蜂群算法(Artificial Bee Colony,ABC)是两种流行且高效的群智能优化算法. GWO具有局部搜索能力强等优势,但存在全局搜索能力弱等缺陷;而ABC具有全局搜索能力强等优点,但存在收敛速度慢等不足.为实现二者优势互补,提出了一种GWO与ABC的混合算法(Hybrid GWO with ABC,HGWOA).首先,使用静态贪心算法替代ABC雇佣蜂阶段中的动态贪心算法来强化探索能力,同时为弥补其收敛速度降低的不足,提出一种新型的搜索蜜源方式;然后,去掉影响收敛速度的侦查蜂阶段,在雇佣蜂阶段再添加反向学习策略,以避免搜索陷入局部最优;最后,为了平衡以上雇佣蜂阶段的探索能力,在观察蜂阶段,自适应融合GWO,以便增强开采能力和提高优化效率.大量的函数优化和聚类优化的实验结果表明,与state-of-the-art方法相比,HGWOA具有更好的优化性能及更强的普适性,且能更好地解决聚类优化问题.
基金Natural Science Foundation of China(51706093)Natural Science Foundation of Jiangsu Province(BK20181308)Fundamental Research Funds for the Central Universities(2018B45414)。
文摘为了提高求解多目标优化问题的Pareto解集的效率,建立了适用于多维、多目标优化问题的多目标蚁群算法(Multi-objective Ant Colony Algorithm,简称MACA)。该算法首先修正了蚁群算法的信息素更新机制和转移概率,然后改进了蚂蚁的行进策略,即提出了依概率选择搜索策略。最后,应用该算法对某型号固液混合火箭发动机系统进行了优化设计。计算结果表明,多目标蚁群算法获得的Pareto解集分布均匀、散布范围广,可以有效解决多目标优化问题,能为决策者进行目标权衡提供充分依据。
