基于策略自适应的多目标差分进化算法及其应用

为提高多目标差分进化算法求解多目标优化问题的能力,提出一种基于策略自适应的多目标差分进化算法（multi-objective differential evolution algorithm based on self-adaptive strategy,MODE-SS）。该算法采用超体积（hyper-volume,HV）对变异策略进行性能评价,并实现变异策略的自动选择;使用动态调整的二项式交叉策略和模拟二进制交叉（simulated binary crossover,SBX）策略实现全局搜索与局部搜索的平衡。通过与其他六种多目标进化算法在10个测试函数上的性能比较,结果表明MODE-SS算法的整体性能要好于其他所比较算法。最后,将MODE-SS算法用于求解海铁联运能耗优化问题,所得结果能够为决策者提供多种可行方案。     

Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and biochemical dynamic optimization problems

Each mutation operator of differential evolution (DE) algorithm is generally suitable for certain specific types of multi-objective optimization problems (MOPs) or particular stages of the evolution. To automatically select an appropriate mutation operator for solving MOPs in different phases of the evolution, a multi-objective differential evolution with performance-metric-based self-adaptive mutation operator (MODE-PMSMO) is proposed in this study. In MODE-PMSMO, a modified inverted generational distance (IGD) is utilized to evaluate the performance of each mutation operator and guide the evolution of mutation operators. The proposed MODE-PMSMO is then compared with seven multi-objective evolutionary algorithms (MOEAs) on five bi-objective and five tri-objective optimization problems. Generally, MODE-PMSMO exhibits the best average performance among all compared algorithms on ten MOPs. Additionally, MODE-PMSMO is employed to solve four typical multi-objective dynamic optimization problems in chemical and biochemical processes. Experimental results indicate that MODE-PMSMO is suitable for solving these actual problems and can provide a set of nondominated solutions for references of decision makers.     

基于混合差分进化算法和熵权TOPSIS的多目标(Q, r)库存模型

讨论随机需求环境下的多目标库存控制问题,构建以成本、缺货率和缺货量3个指标最小化为准则的多目标（Q,r）库存模型．为了求出决策者需要的Pareto前沿,设计了基于遗传和差分进化算法的混合智能算法来产生非支配解,进而利用基于熵权的TOPSIS方法对最优解进行排序,此结果可为管理者提供有益的决策参考．     

基于自适应差分进化的常压塔轻质油产量多目标优化

常压塔轻质油产量最大化是提高企业效益的重要途径之一.为了适应市场需求和价格变化,生产高需求与高价值的轻质油产品,提出一种基于自适应差分进化的常压塔轻质油产量多目标优化算法.该算法采用惩罚边界交叉法的分解方法,在种群变异阶段引入择优学习算子来改进传统变异算子随机选取个体或者单纯选取最好个体的随机性和盲目性,利用自适应策略逐渐改变交叉变异算子.将改进算法应用于3种测试函数和实际炼油厂常压塔轻质油产量优化,结果表明所提出的算法在测试函数上具有明显优势,并能有效提高常压塔轻质油产量,验证了所提算法的有效性.     

Classification-based self-adaptive differential evolution with fast and reliable convergence performance

To avoid the problems of slow and premature convergence of the differential evolution (DE) algorithm, this paper presents a new DE variant named p-ADE. It improves the convergence performance by implementing a new mutation strategy “DE/rand-to-best/pbest”, together with a classification mechanism, and controlling the parameters in a dynamic adaptive manner, where the “DE/rand-to-best/pbest” utilizes the current best solution together with the best previous solution of each individual to guide the search direction. The classification mechanism helps to balance the exploration and exploitation of individuals with different fitness characteristics, thus improving the convergence rate. Dynamic self-adaptation is beneficial for controlling the extent of variation for each individual. Also, it avoids the requirement for prior knowledge about parameter settings. Experimental results confirm the superiority of p-ADE over several existing DE variants as well as other significant evolutionary optimizers.     

Exploring dynamic self-adaptive populations in differential evolution

Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong's F1–F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.     

数据驱动选择策略的多目标差分进化算法

针对多目标差分进化算法求解复杂多目标优化问题时,最优解选择策略中非支配排序计算复杂度高的问题,提出一种数据驱动选择策略的多目标差分进化(MODE-DDSS)算法.首先,设计多目标差分进化算法的优化解排序等级评估准则,建立基于评估准则的优化解排序等级评估库;其次,设计基于优化解双向搜索机制和无重复比较机制的数据驱动选择策略,实现优化解的高效搜索和快速排序;最后,构建数据驱动选择策略的多目标差分进化算法,降低算法在最优解选择操作中的时间复杂度,提高算法的寻优效率.实验结果表明,所提出的MODE-DDSS算法能够有效减少最优解在选择过程中的比较次数,提升多目标差分进化算法解决复杂多目标优化问题的寻优效率.     

改进的排序变异多目标差分进化算法

针对多目标差分进化算法在求解问题时收敛速度慢和均匀性欠佳的问题,提出了一种改进的排序变异多目标差分进化算法（MODE-IRM）。该算法将参与变异的三个父代个体中的最优个体作为基向量,提高了排序变异算子的求解速度;另外,算法采用反向参数控制方法在不同的优化阶段动态调整参数值,进一步提高了算法的收敛速度;最后,引入了改进的拥挤距离计算公式进行排序操作,提高了解的均匀性。采用标准多目标优化问题ZDTl~ZDT4,ZDT6和DTLZ6~DTLZ7进行仿真实验:MODE-IRM在总体性能上均优于MODE-RMO和PlatEMO平台上的MOEA/D-DE、RM-MEDA以及IM-MOEA;在世代距离（GD）、反向世代距离（IGD）和间隔指标（SP）性能度量指标方面,MODE-IRM在所有优化问题上的均值和方差均明显小于MODE-RMO。实验结果表明MODE-IRM在收敛性和均匀性指标上明显优于对比算法。     

Performance comparison of self-adaptive and adaptive differential evolution algorithms

Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, which use different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.     

A self-adaptive differential evolution algorithm for binary CSPs

A novel self-adaptive differential evolution (SADE) algorithm is proposed in this paper. SADE adjusts the mutation rate F and the crossover rate CR adaptively, taking account of the different distribution of population. In order to balance an individual’s exploration and exploitation capability for different evolving phases, F and CR are equal to two different self-adjusted nonlinear functions. Attention is concentrated on varying F and CR dynamically with each generation evolution. SADE maintains the diversity of population and improves the global convergence ability. It also improves the efficiency and success rate and avoids the premature convergence. Simulation and comparisons based on test-sets of CSPs demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.     

Microblog summarization using self-adaptive multi-objective binary differential evolution

Social media platforms become paramount for gathering relevant information during the occurrence of any natural disaster. Twitter has emerged as a platform which is heavily used for the purpose of communication during disaster events. Therefore, it becomes necessary to design a technique which can summarize the relevant tweets and thus, can help in the decision-making process of disaster management authority. In this paper, the problem of summarizing the relevant tweets is posed as an optimization problem where a subset of tweets is selected using the search capability of multi-objective binary differential evolution (MOBDE) by optimizing different perspectives of the summary. MOBDE deals with a set of solutions in its population, and each solution encodes a subset of tweets. Three versions of the proposed approach, namely, MOOTS1, MOOTS2, and MOOTS3, are developed in this paper. They differ in the way of working and the adaptive selection of parameters. Recently developed self-organizing map based genetic operator is explored in the optimization process. Two measures capturing the similarity/dissimilarity between tweets, word mover distance and BM25 are explored in the optimization process. The proposed approaches are evaluated on four datasets related to disaster events containing only relevant tweets. It has been observed that all versions of the developed MOBDE framework outperform the state-of-the-art (SOA) techniques. In terms of improvements, our best-proposed approach (MOOST3) improves by 8.5% and 3.1% in terms of ROUGE??2 and ROUGE?L, respectively, over the existing techniques and these improvements are further validated using statistical significance t-test.

     

分阶段二次变异的多目标混沌差分进化算法

提出一种结合分阶段二次变异和混沌理论的改进差分进化(DE)算法,以解决多目标约束优化问题.其核心思想是,在DE进化前期采用基于非支配解的随机二次变异来提高算法的全局寻优能力,进化后期采用基于非支配解的混沌二次变异来提高DE的局部寻优能力.通过对典型测试问题的仿真实验验证了所提出的算法能在全局搜索性能与局部搜索性能之间维持较好平衡,而且保持了DE算法的简洁性能,其收敛性、分布度和均衡性均优于标准DE.     

结合保优策略和移民策略的自适应遗传算法

针对基本遗传算法具有早熟性收敛、寻优时间长及局部搜索能力差的问题,分析产生这些问题的原因。结合最优保存策略和移民策略,提出基于种群平均适应度信息的遗传算法自适应算子的改进方案,并对改进遗传算法的收敛性予以证明。仿真结果表明,改进遗传算法在搜索效率、搜索精度和克服早熟收敛现象方面均有明显的优越性。     

A self-adaptive differential evolution algorithm for continuous optimization problems

This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.     

多目标强度Pareto 混沌差分进化算法

提出一种多目标强度Pareto混沌差分进化算法(SPCDE).首先利用Tent映射进行种群的混沌初始化,采用一种基于均匀排挤机制的截断排挤操作和混沌替换操作进行种群的环境选择操作;然后基于一种变缩放因子的差分变异策略进行变异操作,通过计算支配关系得到变异个体;最后通过支配关系的计算和环境选择操作进行进化选择操作并得到子代个体.以上操作不仅提高了算法的收敛性能,而且保证了Pareto最优解的均匀分布性.数值实验结果表明了该算法的有效性.     

聚类问题的自适应杂交差分演化模拟退火算法

针对K-均值聚类算法对初始值敏感和易陷入局部最优的缺点,提出了一个基于自适应杂交差分演化模拟退火的K-均值聚类算法。该算法以差分演化算法为基础,通过模拟退火算法的更新策略来增强全局搜索能力,并运用自适应技术来选择学习策略、确定算法的关键参数。实验结果表明,该算法能较好地克服传统K-均值聚类算法的缺点,具有较好的全局收敛能力,且算法稳定性强、收敛速度快,将新算法与传统的K-均值聚类算法以及最近提出的几个同类聚类算法进行了比较。     

Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation

Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.     

基于快速自适应差分进化算法的电力系统经济负荷分配

提出一种求解复杂电力系统经济负荷分配问题的快速自适应差分进化算法(FSADE).从矢量运算角度对变异算子进行分析,提出了一种改进的变异算子,大大提高了算法的收敛速率.根据个体的进化过程,引入自学习机制,对个体的变异和交叉概率常数进行自适应地调整,提高了算法的鲁棒性.3个不同规模的算例仿真结果表明,与其他4种典型智能优化算法相比, FSADE具有更好的计算精度和计算速度,是一种求解电力系统经济负荷分配问题的有效方法.     

改进自适应差分进化算法求解大规模整数任务分配

针对0-1任务规划模型存在维数灾维的问题,提出一种基于改进自适应差分进化(SADE)算法的大规模整数任务分配算法。首先,将任务分配的0-1规划模型转化整数规划模型,不仅大幅减少了优化变量的维数,还减少了整式约束条件;然后,将常用的变异算子DE/rand/1/bin和DE/best/2/bin结合起来组成新的自适应变异算子,使得自适应差分进化算法既有较快的收敛速度,又降低了变异算子对具体问题的依赖;并用改进自适应差分进化算法求解整数规划。最后,通过典型的任务分配实例验证了算法在优化大规模任务分配的有效性和快速性。