A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments.     

动态规划-粒子群算法在水库优化调度中的应用

水库优化调度是一个典型的具有多约束条件的、动态的、非线性的优化问题。针对这些问题,利用动态规划-粒子群(DP-PSO)算法加以求解。利用动态规划中的多阶段最优策略原理,将水库优化调度问题转化为多阶段决策子问题,各个子问题采用粒子群算法优化求解。数值实验表明,在计算时段较多时,DP-PSO算法计算的可靠性明显优于一般的动态规划(DP)算法,在计算时间上,DP-PSO算法用时较动态规划-遗传算法(DP-GA)少。     

动态分级的并行约束优化进化算法

提出一种动态分级的并行进化算法用于求解约束优化问题。该算法首先利用佳点集方法初始化种群。在进化过程中,将种群个体分为两个子种群,分别用于全局和局部搜索,并根据不同的搜索阶段动态调整各种级别中并行变量的数目。标准测试问题的实验结果表明了该算法的可行性和有效性。     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题，为了提高拣货作业效率，提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先，建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型；然后，考虑双重优化的复杂性，设计了一种嵌套遗传算法对模型进行求解，外层不断优化订单分批结果，内层根据外层订单分批结果优化拣货路径。算例结果表明，与传统的订单分步优化、分批分步优化策略相比，所提策略的拣货时间分别减少了45.6%、6%，基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能，分别对10、20、50张订单规模的算例进行仿真实验，结果表明，随着订单量的增加，整体拣货距离和时间进一步减少，拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题，为配送中心拣选系统的优化提供依据。     

Communication latency tolerant parallel algorithm for particle swarm optimization

Particle swarm optimization (PSO) algorithm is a population-based algorithm for finding the optimal solution. Because of its simplicity in implementation and fewer adjustable parameters compared to the other global optimization algorithms, PSO is gaining attention in solving complex and large scale problems. However, PSO often requires long execution time to solve those problems. This paper proposes a parallel PSO algorithm, called delayed exchange parallelization, which improves performance of PSO on distributed environment by hiding communication latency efficiently. By overlapping communication with computation, the proposed algorithm extracts parallelism inherent in PSO. The performance of our proposed parallel PSO algorithm was evaluated using several applications. The results of evaluation showed that the proposed parallel algorithm drastically improved the performance of PSO, especially in high-latency network environment.     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题,为了提高拣货作业效率,提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先,建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型;然后,考虑双重优化的复杂性,设计了一种嵌套遗传算法对模型进行求解,外层不断优化订单分批结果,内层根据外层订单分批结果优化拣货路径。算例结果表明,与传统的订单分步优化、分批分步优化策略相比,所提策略的拣货时间分别减少了45.6%、6%,基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能,分别对10、20、50张订单规模的算例进行仿真实验,结果表明,随着订单量的增加,整体拣货距离和时间进一步减少,拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题,为配送中心拣选系统的优化提供依据。     

一种自适应惯性权重的并行粒子群聚类算法

针对K-means聚类算法和基于遗传（GA）的聚类算法的一些缺点,及求解实优化问题时粒子群算法优于遗传算法这一事实,提出了一种自适应惯性权重的并行粒子群聚类算法。理论分析和实验表明,该算法在收敛速度和收敛精度方面明显优于基于遗传算法的聚类方法。     

分层并行遗传算法和遗传复合形算法及其应用

基于复合形算法、遗传算法、分层和并行思想,设计了一种求解复杂多目标、多约束和多变量工程优化问题的分层并行遗传或复合形算法,编制了界面友好和计算可靠性高的VC++软件。对于一类复杂三多工程综合优化问题,进行了遗传算法、复合形算法、分层并行遗传算法和分层并行遗传复合形算法的大量计算,结果表明：分层并行遗传算法计算效率最高;为解决复杂的三多工程综合优化问题提供了有效的可行方法。     

一种新的求解动态连续优化的分层粒子群算法

为了高效求解动态连续优化问题,提出一种分层粒子群优化算法。该算法将动态函数定义域分成Q个子空间,每个空间用一个粒子群作为第一层进行独立搜索,Q个子空间的最优粒子再组成一个全局粒子群进行全局搜索,以达到全局牵引的作用,同时提出探测环境和响应环境的策略。利用经典的动态函数对算法进行测试,结果表明所提出算法能够迅速适应环境变化和跟踪最优解的变化,效果令人满意。     

基于递阶遗传算法的并行多机调度优化

针对最小化完工时间的等同和非等同并行多机调度一类问题，提出了一种递阶遗传算法。该算法根据问题的特点，采用一种递阶编码方案，此编码与调度方案一一对应。用递阶遗传算法优化并行多机调度不需设计专门的遗传算子，操作简单。计算结果表明，递阶遗传算法是有效的，能适用于大规模等同和非等同并行多机调度问题。     

A parallel double-level multiobjective evolutionary algorithm for robust optimization

Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.     

动态分级中心引力约束优化算法及工程应用

结合非固定多段罚函数处理约束条件,提出一种动态分级中心引力优化算法用于求解约束优化问题。该算法利用佳点集初始化个体以保证种群的多样性。在每次迭代过程中将种群分为两个子种群,分别用于全局搜索和局部搜索,根据搜索阶段动态调整子种群个体数目。对几个标准的测试问题和工程优化问题进行数值实验,结果表明该算法能处理不同的约束优化问题。     

Hybrid parallel chaos optimization algorithm with harmony search algorithm

The application of chaotic sequences can be an interesting alternative to provide search diversity in an optimization procedure, named chaos optimization algorithm (COA). Since the chaotic motion is pseudo-randomness and chaotic sequences are sensitive to the initial conditions, the search ability of COA is usually effected by the starting values. Considering this weakness, parallel chaos optimization algorithm (PCOA) is studied in this paper. To obtain optimum solution accurately, harmony search algorithm (HSA) is integrated with PCOA to form a novel hybrid algorithm. Different chaotic maps are compared and the impacts of parallel parameter on the hybrid algorithm are discussed. Several simulation results are used to show the effective performance of the proposed hybrid algorithm.     

基于杂合机制的免疫遗传算法在动态问题中的应用

针对遗传算法在求解动态问题时存在多样性缺失,无法快速响应环境变化的问题,提出一种基于杂合子机制的免疫遗传算法．该算法借鉴免疫系统中多样性与记忆机理,从保持等位基因多样性出发,在免疫变异中引入杂合映射机制,使种群能够探索更大的解空间．同时,通过引入记忆策略,使算法迅速跟踪最优解变化轨迹．该方法在动态０-１优化问题的求解中取得了较好的效果．     

基于深度自适应动态规划的孤岛主动配电网发电控制与优化一体化算法

随着多种分布式新能源的并网,如风电与光伏发电、生物质能发电、储能与电动汽车等,传统情况下孤岛配电网的发电控制方法已很难达到高品质频率稳定控制的要求.为解决此问题,本文提出了一种新颖的深度自适应动态规划算法.该算法将自适应动态规划算法中的神经网络替换为机器学习领域中的深度神经网络,并在其中添加深度模型预测网络.所提算法能一次性完成传统模式下"发电控制算法+指令优化分配算法"共同完成的工作.最后,为验证深度自适应动态规划算法的鲁棒性,设计了多种配电网的仿真实验,即正常情况、"即插即用"启停机情况、通讯故障情况、全天扰动仿真情况、变拓扑结构的孤岛配网情况和变参数模型的仿真,设置的总仿真时长达25年.仿真结果验证了所设计的深度自适应动态规划算法有效性、可行性与强鲁棒性.     

动态优化问题的瞬态自适应麻雀搜索算法求解

动态优化普遍存在于工业过程控制领域,是实现系统稳态与产值最大化的重要手段,应用并发展更加高效的动态优化方法逐渐成为了当前研究的热点。鉴于此,提出一种基于瞬态自适应麻雀搜索算法（TASSA）的动态优化问题求解方案。首先,分析了原始麻雀搜索算法的缺陷,为了提升全局勘探能力,引入瞬态搜索策略指导加入者的寻优过程;其次,采用随迭代而变化的惯性权重调节具体的搜索方式,增强了算法的动态适应能力,并通过九组基准函数的数值测试确认了改进策略的有效性。最后,采用时域等分的方式,在控制变量参数化（CVP）的框架下利用TASSA对三组典型的动态优化问题进行求解,对比不同文献中的方法,所提算法取得了更精确的结果。     

基于自适应小生境递阶遗传算法的RBF均衡器

递阶遗传算法(HGA)一次只能确定一个最优个体。采用小生境递阶遗传算法,依据进化信息自适应调整小生境区域,在均衡数据误比特率最低,隐层中心聚类有效性最佳的基础上,可以从多个进化优解中确定出最佳结构的径向基(RBF)神经网络均衡器。仿真结果验证了算法的有效性和稳定性。     

基于并行遗传算法的气球力Snake模型参数优化

针对气球力Snake模型的图像分割效果很大限度上依赖于初始参数的选取,借鉴遗传算法的高效、并行和全局搜索的性能,提出了一种求解气球力Snake模型最优参数的算法。该算法用气球力Snake能量泛函作为目标函数,引入图像相似度函数作为遗传迭代终止准则,采用并行遗传计算进行分割参数寻优。实际医学图像的实验结果表明,算法能避免通过大量实验来人工选取参数的繁琐,也解决了参数选取不当导致的分割结果不理想的问题,可以得到较好的分割效果。     

并行多目标遗传算法在控制系统优化中的应用

为了提高复杂控制系统设计问题中的效率,提出了一种基于主从模型的并行多目标遗传算法的优化器模型。主进程进行各类遗传操作和最优排序操作,所有进程都进行目标函数值和约束函数值的运算操作,并采用动态负载平衡策略。将该优化器应用在飞行器控制系统设计中,显示出了该算法的优良效果。     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.