【摘要】：為了提高該算法求解約束優(yōu)化問題的能力,提出一種新的約束病毒種群搜索算法。首先,提出自適應(yīng)α-level比較策略,以在算法的不同階段充分利用可行個體與不可行個體的有效信息;其次,為了進一步提高算法求解約束優(yōu)化問題的收斂速度和搜索精度,針對算法的病毒擴散行為,提出了結(jié)合反向?qū)W習機制的搜索方程,以提高種群多樣性并加速全局收斂。對CEC2006中13個約束優(yōu)化函數(shù)的對比仿真結(jié)果表明,本文算法在搜索精度、收斂速度以及穩(wěn)定性方面,相比于αSimplex算法、粒子群遺傳算法算法、交叉人工蜂群算法算法以及約束改進差分進化算法算法具有明顯優(yōu)勢。同時將該算法應(yīng)用于無人機協(xié)同實時航跡規(guī)劃約束優(yōu)化問題中,通過仿真實驗并與利用約束改進差分進化算法對這一問題進行求解的方法進行對比,驗證了本文算法在規(guī)劃效率、規(guī)避威脅等方面的優(yōu)越性。
[Abstract]:In order to improve the ability of the algorithm to solve constrained optimization problems, a new constrained virus population search algorithm is proposed. Firstly, an adaptive 偽 -level comparison strategy is proposed to make full use of the effective information between feasible and infeasible individuals at different stages of the algorithm. Secondly, in order to further improve the convergence speed and search accuracy of the algorithm for solving constrained optimization problems, In order to improve population diversity and accelerate global convergence, a search equation based on reverse learning mechanism is proposed for the virus diffusion behavior of the algorithm. The simulation results of 13 constrained optimization functions in CEC2006 show that, compared with 偽 Simplex algorithm, the PSO algorithm in this paper is more accurate, faster and more stable than the 偽 Simplex algorithm. Crossover artificial bee colony algorithm and constrained improved differential evolution algorithm have obvious advantages. At the same time, the algorithm is applied to the constrained optimization problem of UAV collaborative real-time track planning. The simulation results are compared with the method of using constrained improved differential evolution algorithm to solve the problem. The superiority of this algorithm in planning efficiency and avoiding threat is verified.
【作者單位】： 空軍工程大學航空航天工程學院;復雜航空系統(tǒng)仿真重點實驗室;95994部隊;
【基金】：國家杰出青年科學基金資助項目(71501184)
【分類號】：TP18

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