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
针对0-1背包问题求解,将离散二进制粒子群优化(Binary Particle Swarm Optimization, BPSO)算法、贪心优化策略和模拟退火算法有机结合,提出了一种改进算法:带贪心优化的混合粒子群和模拟退火(Hybrid optimization algorithm based on t...针对0-1背包问题求解,将离散二进制粒子群优化(Binary Particle Swarm Optimization, BPSO)算法、贪心优化策略和模拟退火算法有机结合,提出了一种改进算法:带贪心优化的混合粒子群和模拟退火(Hybrid optimization algorithm based on the BPSO, the Simulated Annealing (SA) Algorithm and the Combined Greedy Optimization Operator (CGOO), BPSOSA-CGOO)算法.基于新算法,完成了9组不同维度数据的仿真实验.实验结果表明, BPSOSA-CGOO算法能够以较小的种群规模及迭代次数实现0-1背包问题的有效求解,并在问题维度为20维的测试数据中找到优于已知最优解的解;独立重复实验验证了,无论对于低维度还是高维度背包问题, BPSOSA-CGOO算法均能以较高概率命中最优解,提高了高维度背包问题求解的稳定性和可靠性.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.
文摘针对0-1背包问题求解,将离散二进制粒子群优化(Binary Particle Swarm Optimization, BPSO)算法、贪心优化策略和模拟退火算法有机结合,提出了一种改进算法:带贪心优化的混合粒子群和模拟退火(Hybrid optimization algorithm based on the BPSO, the Simulated Annealing (SA) Algorithm and the Combined Greedy Optimization Operator (CGOO), BPSOSA-CGOO)算法.基于新算法,完成了9组不同维度数据的仿真实验.实验结果表明, BPSOSA-CGOO算法能够以较小的种群规模及迭代次数实现0-1背包问题的有效求解,并在问题维度为20维的测试数据中找到优于已知最优解的解;独立重复实验验证了,无论对于低维度还是高维度背包问题, BPSOSA-CGOO算法均能以较高概率命中最优解,提高了高维度背包问题求解的稳定性和可靠性.
