Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application
Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application
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DOI：
10.1016/j.ijleo.2015.12.122
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发表时间：
2016-03
期刊：
影响因子：
3.1
通讯作者：
Hong-tao Liang;Feng-ju Kang
中科院分区：
文献类型：
--
作者：
Hong-tao Liang;Feng-ju Kang
提出了一种基于克隆选择算法（CSA）的舰艇编队防空武器目标分配问题的目标优化方法。本文提出的自适应混沌并行克隆选择算法（ACPCSA）结合混沌理论和并行种群分类的优点，实现了种群初始化和种群更新。该算法创造性地采用混沌再生和混沌扰动来设计种群初始化算子和超突变算子。采用并行种群分类设计各子种群并行机制，根据亲和度保持种群多样性。此外，通过自适应克隆增殖算子、抗体抑制算子和抗体循环补充算子对CSA进行改进，提高了算子的全局优化能力和局部搜索能力。最后，对典型场景进行了仿真，并与其他算法的实现进行了比较。仿真结果表明，该算法在搜索精度和收敛灵活性方面具有良好的优化性能，为舰船编队防空应用中WTA问题的解决提供了有效途径。
This paper presents a novel objective optimization approach based on clonal selection algorithm (CSA) to solve the problems of weapon-target assignment (WTA) in warship formation antiaircraft application. The proposed CSA, namely adaptive chaos parallel clonal selection algorithm (ACPCSA), combines the benefits of chaos theory and parallel population classification to realize the population initialization and population update. In this algorithm, Chaos regeneration and Chaos disturbance are creatively employed to design population initialization operator and hyper-mutation operator. And parallel population classification is adopted to design parallel mechanism for all sub-populations, which can keep the population diversity according to affinity. Besides, CSA is improved by adaptive clonal proliferation operator, antibody inhibition operator and antibody circulation supplement operator, where operators can improve global optimization ability and local searching ability. Finally, typical scenario is performed and compared by implementation of other algorithms. Simulation results show that the proposed ACPCSA has good optimization performance in terms of search accuracy and convergence flexibility, which can provide an effective way to solve WTA in warship formation antiaircraft application.