CAJ | 학술논문

为了有效解决动态多目标优化问题，文中提出了一种新的基于预测策略的动态多目标免疫优化算法。该算法首先采用相似性检测算子较好地检测到环境的变化。同时利用前几个时刻的最优非支配抗体解集建立新的预测模型来预测产生新时刻的初始抗体种群，进一步提高了算法对环境变化的反应能力。此外，通过引入基于两种不同的父代个体选择策略而改进的差分交叉算子来加快算法的收敛速度。文中采用几个典型的标准测试问题验证算法的有效性，实验结果表明，提出的相似性检测算子的预测模型可以提高算法的跟踪能力，而改进的差分交叉算子能够提高算法的收敛性能。
위료유효해결동태다목표우화문제，문중제출료일충신적기우예측책략적동태다목표면역우화산법。해산법수선채용상사성검측산자교호지검측도배경적변화。동시이용전궤개시각적최우비지배항체해집건립신적예측모형래예측산생신시각적초시항체충군，진일보제고료산법대배경변화적반응능력。차외，통과인입기우량충불동적부대개체선택책략이개진적차분교차산자래가쾌산법적수렴속도。문중채용궤개전형적표준측시문제험증산법적유효성，실험결과표명，제출적상사성검측산자적예측모형가이제고산법적근종능력，이개진적차분교차산자능구제고산법적수렴성능。
In this paper,a new dynamic multi-objective immune optimization algorithm based on prediction strategy is proposed for solving dynamic multi-objective optimization problems effec-tively.Firstly a similarity detection operation is used to detect the environment change.Then,a new forecasting model,which is established by the non-dominated antibodies in previous optimum locations,is adopted to generate the initial antibody population in order to improve the ability of responding to the environment change.Moreover,an improved differential evolution crossover operator based on two different selection strategies is introduced to speed the convergence of algorithm.The proposed algorithm is validated on several benchmark testing problems,the experimental result shows that the forecasting model based on the similarity detection operation can improve the tracking ability and the improved differential crossover operation can enhance the convergence.