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1.
约束优化进化算法综述   总被引:3,自引:0,他引:3  
李智勇  黄滔  陈少淼  李仁发 《软件学报》2017,28(6):1529-1546
约束优化进化算法主要研究如何利用进化计算方法求解约束优化问题,是进化计算领城的一个重要研究课题.约束优化问题求解存在约束区域离散、等式约束、非线性约束等挑战,其问题的本质是如何处理可行解与不可行解的关系才能使得算法更高效.本文首先介绍了约束优化问题的定义,然后系统地分析了目前存在的约束优化方法,同时基于约束处理机制将这些方法分为罚函数法、可行性法则、随机排序法、约束处理法、多目标优化法、混合法六类,并从约束处理方法的方面对约束优化进化算法的最新研究进展进行综述.最后,指出约束优化进化算法需进一步研究的方向与关键问题.  相似文献   

2.
约束优化问题广泛存在于科学研究和工程实践中,其对应的约束优化进化算法也成为了进化领域的重要研究方向。约束优化进化算法的本质问题是如何有效地利用不可行解和可行解的信息,平衡目标函数和约束条件,使得算法更加高效。首先对约束优化问题进行定义;然后详细分析了目前主流的约束进化算法,同时,基于不同的约束处理机制,将这些机制分为约束和目标分离法、惩罚函数法、多目标优化法、混合法和其他算法,并对这些方法进行了详细的分析和总结;接着指出约束进化算法亟待解决的问题,并明确指出未来需要进一步研究的方向;最后对约束进化算法在工程优化、电子和通信工程、机械设计、环境资源配置、科研领域和管理分配等方面的应用进行了介绍。  相似文献   

3.
约束优化是多数实际工程应用优化问题的呈现方式.进化算法由于其高效的表现,近年来被广泛应用于约束优化问题求解.但约束条件使得问题解空间离散、缩小、改变,给进化算法求解约束优化问题带来极大挑战.在此背景下,融合约束处理技术的进化算法成为研究热点.此外,随着研究的深入,近年来约束处理技术在复杂工程应用问题优化中得到了广泛发展,例如多目标、高维、等式优化等.根据复杂性的缘由,将面向复杂约束优化问题的进化优化分为面向复杂目标的进化约束优化算法和面向复杂约束场景的进化算法两种类别进行综述,其中,重点探讨了实际工程应用的复杂性对约束处理技术的挑战和目前研究的最新进展,并最后总结了未来的研究趋势与挑战.  相似文献   

4.
多目标优化与自适应惩罚的混合约束优化进化算法   总被引:5,自引:0,他引:5  
甘敏 《控制与决策》2010,25(3):378-382
提出一种多目标优化与自适应惩罚函数相结合的方法来处理约束优化问题.首先利用多目标优化方法提取当前群体中的主要信息;然后进一步用自适应惩罚函数选出最有价值的信息.将这种约束处理技术与一种基于群的算法生成器模型相结合,即可得到一种新的约束优化进化算法.选取10个标准测试函数对新算法的性能进行数值实验,结果表明了所提出方法的有效性和较强的稳健性,与其他尖端算法相比得到了相似或更优的结果.  相似文献   

5.
提出一种改进的差分进化算法用于求解约束优化问题.该算法在处理约束时不引入惩罚因子,使约束处理问题简单化.利用佳点集方法初始化个体以维持种群的多样性.结合差分进化算法两种不同变异策略的特点,对可行个体与不可行个体分别采用DE/best/1变异策略和DE/rand/1策略,以提高算法的全局收敛性能和收敛速率.用几个标准的Benchmark问题进行了测试,实验结果表明该算法是一种求解约束优化问题的有效方法.  相似文献   

6.
利用增广Lagrange罚函数处理问题的约束条件,提出了一种新的约束优化差分进化算法。基于增广Lagrange惩罚函数,将原约束优化问题转换为界约束优化问题。在进化过程中,根据个体的适应度值将种群分为精英种群和普通种群,分别采用不同的变异策略,以平衡算法的全局和局部搜索能力。用10个经典Benchmark问题进行了测试,实验结果表明,该算法能有效地处理不同的约束优化问题。  相似文献   

7.
基于种群个体可行性的约束优化进化算法   总被引:4,自引:0,他引:4  
提出一种新的求解约束优化问题的进化算法.该算法在处理约束时不引入惩罚因子,使约束处理问题简单化.基于种群中个体的可行性,分别采用3种不同的交叉方式和混合变异机制用于指导算法快速搜索过程.为了求解位于边界附近的全局最优解,引入一种不可行解保存和替换机制,允许一定比例的最好不可行解进入下一代种群.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.  相似文献   

8.
提出一种用于求解约束优化问题的自适应佳点集进化算法.新算法利用佳点集原理设计多点交叉算子,该交叉算子能够根据父代个体的相似度自适应调整交叉点的位置和子代个体的数目,产生具有代表性的子代个体.在约束处理技术上,改进了Deb的三条比较准则,提出一种新的适应度函数用于比较个体优、劣的比较准则.通过对13个标准测试函数的试验比较验证了新算法的有效性和稳健性.  相似文献   

9.
约束多目标进化算法(CMOEAs)能够同时处理多个相互冲突的目标函数和约束条件,引导种群逼向可行域的最优解,受到了研究者的广泛重视。首先介绍了约束多目标优化问题(CMOPs)的相关定义和多目标进化算法(MOEAs)的三种分类;其次,系统地分析了当前CMOEAs中约束处理机制,凝练出当前主要的四种约束处理方法;然后,从基于支配、基于指标、基于分解三个方面对CMOEAs的研究进展进行了详细综述;最后,指明了CMOEAs存在的挑战和未来研究方向。  相似文献   

10.
动态非线性约束优化是一类复杂的动态优化问题,其求解的困难主要在于如何处理问题的约束及时间(环境)变量。给出了一类定义在离散时间(环境)空间上的动态非线性约束优化问题的新解法,从问题的约束条件出发构造了一个新的动态熵函数,利用此函数将原优化问题转化成了两个目标的动态优化问题。进一步设计了新的杂交算子和带局部搜索的变异算子,提出了一种新的多目标优化求解进化算法。通过对两个动态非线性约束优化问题的计算仿真,表明该算法是有效的。  相似文献   

11.
A considerable number of constrained optimization evolutionary algorithms (COEAs) have been proposed due to increasing interest in solving constrained optimization problems (COPs) by evolutionary algorithms (EAs). In this paper, we first review existing COEAs. Then, a novel EA for constrained optimization is presented. In the process of population evolution, our algorithm is based on multiobjective optimization techniques, i.e., an individual in the parent population may be replaced if it is dominated by a nondominated individual in the offspring population. In addition, three models of a population-based algorithm-generator and an infeasible solution archiving and replacement mechanism are introduced. Furthermore, the simplex crossover is used as a recombination operator to enrich the exploration and exploitation abilities of the approach proposed. The new approach is tested on 13 well-known benchmark functions, and the empirical evidence suggests that it is robust, efficient, and generic when handling linear/nonlinear equality/inequality constraints. Compared with some other state-of-the-art algorithms, our algorithm remarkably outperforms them in terms of the best, mean, and worst objective function values and the standard deviations. It is noteworthy that our algorithm does not require the transformation of equality constraints into inequality constraints  相似文献   

12.
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization.  相似文献   

13.
This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which, from the author’s perspective, constitute good potential areas for future research, namely, constraint-handling techniques, incorporation of user’s preferences and parameter control. This information is expected to be useful for those interested in pursuing research in this area.  相似文献   

14.
一种新型的差分演化算法及其应用研究   总被引:1,自引:0,他引:1  
提出了一种新的基于简单多样性规则的改进差分演化算法,并把它运用于约束全局最优化问题的求解中。新算法的特征是: 1)提出一种新的混合自适应交叉变异算子,以增强算法的搜索能力; 2)采用具有保持群体多样性的约束函数处理技术; 3)简化基本差分演化算法的缩放因子,尽量减少算法的控制参数,方便工程人员的使用。通过对13个标准测试函数进行测试,并与其他演化算法结果进行比较。实验结果表明,新算法在求解精度和稳定性具有很好的性能,而且其函数平均评价次数要低于所比较的其他演化算法。  相似文献   

15.
聚类佳点集交叉的约束优化混合进化算法   总被引:2,自引:0,他引:2  
提出一种基于聚类佳点集多父代交叉和自适应约束处理技术的混合进化算法用于求解约束优化问题.新算法的主要特点是:在搜索机制方面,利用佳点集方法构造初始化种群,使个体能够均匀地分布在整个搜索空间.然后根据父代个体的相似度将种群个体进行聚类分析,从聚类中随机选择个体进行佳点集多父代交叉操作,利用多个父代个体所携带的信息产生新的具有代表性的子代个体,能够维持和增加种群的多样性.另外,引入局部搜索策略以提高算法局部搜索能力和收敛速度.在约束处理技术上,新算法引入了一个自适应约束处理技术,即根据当前种群中可行解的比例自适应选择不同的个体比较准则.通过15个标准测试函数验证了新算法的有效性.  相似文献   

16.
In this study, we introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction.  相似文献   

17.
Over the last two decades, many different evolutionary algorithms (EAs) have been introduced for solving constrained optimization problems (COPs). Due to the variability of the characteristics in different COPs, no single algorithm performs consistently over a range of practical problems. To design and refine an algorithm, numerous trial-and-error runs are often performed in order to choose a suitable search operator and the parameters. However, even by trial-and-error, one may not find an appropriate search operator and parameters. In this paper, we have applied the concept of training and testing with a self-adaptive multi-operator based evolutionary algorithm to find suitable parameters. The training and testing sets are decided based on the mathematical properties of 60 problems from two well-known specialized benchmark test sets. The experimental results provide interesting insights and a new way of choosing parameters.  相似文献   

18.
In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.  相似文献   

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