一种新的基于预测的动态多目标进化算法

New prediction strategy based evolutionary algorithm for dynamic multi-objective optimization

动态多目标优化问题(DMOPs)是指目标函数随时间变化的一类问题,算法求解的目标是持续跟踪移动的Pareto最优解集或Pareto最优前沿。基于预测的方法受到格外的关注,然而这些方法多使用历史环境信息进行预测,考虑到使用历史信息预测会存在预测不准确的问题,加强新环境信息的挖掘和利用,提出了一种新的基于预测的动态多目标进化算法,该算法主要包括两个核心部分,分别记为响应机制和加速机制。响应机制在环境变化后重新初始化群体,一部分的个体由预测策略产生,以生成靠近下一环境Pareto最优解集的个体来提高算法的寻优能力,剩余部分个体采用局部搜索策略生成以增加种群多样性。加速机制用于静态优化过程以提高算法收敛速度。最后,将动态多目标进化算法与其他3种先进的动态多目标优化算法在具有不同动态特征的一系列测试函数上进行实验对比,结果表明,动态多目标进化算法相比其他3个算法在求解动态多目标优化问题中更具有优势。

Dynamic multi-objective optimization problems(DMOPs)where the environments change over time require that an evolutionary algorithm be able to continuously track the moving Pareto set or Pareto front.Response strategies based prediction has received much attention.However,these strategies mostly use historical environmental information for prediction,which will make the predicted results inaccurate.In this paper,we strengthen the mining and utilization of new environmental information and propose a new prediction strategy based evolutionary algorithm for dynamic multi-objective optimization(RAM),which includes mainly two core parts,namely,response mechanism and acceleration mechanism.The response mechanism reinitializes the population after the environmental changes,some individuals are generated by the prediction strategy,which is close to the new environmental PS to improve the optimization ability of this algorithm,and the remaining individuals are generated by the local search strategy to increase the population diversity.The acceleration mechanism is used in the static optimization process to accelerate the convergence speed of the RAM.Finally,the RAM is compared with other three advanced dynamic multi-objective optimization algorithms on a series of test functions with different dynamic characteristics.The results show that the RAM has more advantages than other three algorithms in solving dynamic multi-objective optimization problems.