A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization

A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopted for evolutionary computing is outlined. The procedures for executing the computational optimization are provided. A large number of constrained and unconstrained test functions have been utilized to verify this new scheme. Its performances on convergence and diversity have been statistically examined and comparisons with other evolutionary algorithms are carried out. It has been discovered that this new scheme is robust and able to provide outcomes quickly and accurately. A stringent measure of binary-indicator is also applied for algorithm classification. The outcome from this test indicates that the JG paradigm is a very competitive scheme for multiobjective optimization and also a compatible evolutionary computing scheme when speed in convergence, diversity, and accuracy are simultaneously required.     

A Multiobjective Optimization Model for Dynamic Reconfiguration of Ring Topologies with Stochastic Load

In this paper we describe a heuristic procedure to generate solutions to a multiobjective stochastic, optimization problem for a dynamic telecommunications network. Generating Pareto optimal solutions can be difficult since the optimization problem is computationally challenging and moreover the network must be reconfigured in near real time, for example, to recover connectivity after a severe weather event. There are two main contributions of this paper. First, we show mathematically how a certain deterministic equivalent optimization problem can be solved instead of the stochastic one, thus facilitating computations. Second, we test our heuristic under a wide set of simulated conditions (e.g., atmospheric obscuration due to differing levels of cloud cover, different demand patterns) and show that it achieves near Pareto optimality in a short amount of time.     

基于多粒子群协同的动态多目标优化算法及应用

在现实生活中大多数多目标优化问题都随时间变化,这就要求优化算法在时间约束内快速找到动态变化Pareto最优解或Pareto边界.基于此,提出一种基于多种群协同的动态多目标粒子群改进算法,旨在利用多种群竞争和协作两种模式互相配合,从而达到快速高效求解动态多目标优化问题的目的,多种群竞争模式主要任务是对解空间进行“勘探”搜索,当竞争失效后,自适应切换到协作模式对解空间进行“开采”搜索.通过对多种群协同搜索概率分析,证明多种群相比单种群具有更高的搜索效率,通过对3类动态多目标测试函数仿真,验证了改进算法的有效性;最后将该方法应用于动态系统PID控制器的参数整定上,得到了较优的控制参数,取得满意的控制效果.     

Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization

A multiple-swarm multiobjective particle swarm optimization (PSO) algorithm, named dynamic multiple swarms in multiobjective PSO, is proposed in which the number of swarms is adaptively adjusted throughout the search process via the proposed dynamic swarm strategy. The strategy allocates an appropriate number of swarms as required to support convergence and diversity criteria among the swarms. Additional novel designs include a PSO updating mechanism to better manage the communication within a swarm and among swarms and an objective space compression and expansion strategy to progressively exploit the objective space during the search process. Comparative study shows that the performance of the proposed algorithm is competitive in comparison to the selected algorithms on standard benchmark problems. In particular, when dealing with test problems with multiple local Pareto fronts, the proposed algorithm is much less computationally demanding. Sensitivity analysis indicates that the proposed algorithm is insensitive to most of the user-specified design parameters.     

动态系统控制器的多目标优化设计方法

1　引言在控制系统理论与应用中 ,有两种多目标优化控制的提法 ,其一是将控制系统的传递函数的 l1 范数、H∞ 范数和 H2 范数作为目标函数的多目标优化控制 ,这方面的工作可参考文 [1 ]及所引用文献 ;其二是将控制系统的一些时域性能指标作为优化目标的多目标优化控制 .本文考虑后一种形式的多目标优化控制问题 .众所周知 ,对一个动态系统设计控制器时 ,一般有多个指标需要考虑 .最优控制器的设计可以是将这些指标加相应的权系数作和求解最小问题 .但是 ,这样处理存在两个问题 :对于某些问题 ,采取重要目标加大权往往达不到期望的目的 ;另…     

Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization

Most multimodal multi-objective evolutionary algorithms(MMEAs) aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However, in real-world problems, decision makers(DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as generalized MMOPs. Moreover, most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to de...     

基于自适应资源分配池的竞争合作群协同优化算法

合作协同优化是目前针对大规模优化问题的最有前景的算法之一,该算法通过分而治之策略划分子问题,以进行协同进化.不同的子问题根据演化状态的不同对整体改善的贡献大小也不一致,因此均匀分配计算资源会造成浪费.针对上述问题,提出一种新颖的基于自适应资源分配池策略和基于竞争的群优化集成的竞争合作群协同优化算法.首先,考虑到子问题的...     

基于最大最小适应度函数的多目标粒子群算法

针对多目标优化问题提出了一种基于最大最小适应度函数（F_maximin）的粒子群算法，将此算法简称为IMPSO。它在求解多目标问题的非劣解前沿（Pareto Front）时表现出很好的性能。通过经典测试函数计算表明该算法保证收敛到多目标优化问题的Pareto最优前沿；同时，使用两个性能指标（GD和Diversity）验证了此算法优于其他的多目标粒子群优化算法。     

A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization

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     

耦合系统协同进化多学科设计优化算法研究

针对耦合系统的优化问题,为提高系统的自适应性,提出了协同进化多学科设计优化算法.算法受生态系统内生活在同一地域物种间既有竞争又有合作的协同进化关系的启发,将协同进化算法的分解与协作思想和MDO方法的分解与协同思想相结合,在域值分解的基础上将复杂耦合系统分解成学科间相对独立并保持自治的多学科系统.隐含迭代策略协调学科间耦合约束的一致性.建立了多个优化过程模型.并提出的算法应用于多学科耦合系统进行测试,优化结果与MDF、IDF和AAO三种单级优化方法的优化结果进行比较,显示有较强的搜索能力以及较快的收敛速度和自适应性.     

A Tabu-Based Exploratory Evolutionary Algorithm for Multiobjective Optimization

This paper presents an exploratorymultiobjective evolutionary algorithm (EMOEA)that integrates the features of tabu search andevolutionary algorithm for multiobjective (MO)optimization. The method incorporates the taburestriction in individual examination andpreservation in order to maintain the searchdiversity in evolutionary MO optimization,which subsequently helps to prevent the searchfrom trapping in local optima as well as topromote the evolution towards the globaltrade-offs concurrently. In addition, a newlateral interference is presented in the paperto distribute nondominated individuals alongthe discovered Pareto-front uniformly. Unlikemany niching or sharing methods, the lateralinterference can be performed without the needof parameter settings and can be flexiblyapplied in either the parameter or objectivedomain. The features of the proposed algorithmare examined based upon three benchmarkproblems. Experimental results show that EMOEAperforms well in searching and distributingnondominated solutions along the trade-offsuniformly, and offers a competitive behavior toescape from local optima in a noisyenvironment.     

M-精英协同进化数值优化算法

为了解决高维无约束数值优化问题,借鉴协同进化和精英策略的思想,提出了M-精英协同进化算法.该算法认为,适应度较高的个体群(称为精英种群)在整个种群进化中起着主导作用.算法将整个种群划分为由M个精英组成的精英种群和由其余个体组成的普通种群这样两个子种群,依次以M个精英为核心(称为核心精英)来选择成员以组建M个团队.若选中的团队成员是其他精英,则该成员与核心精英利用所定义的协作操作来交换信息;若团队成员选自普通种群,则由核心精英对其进行引导操作.其中,协作操作和引导操作由若干不同类型的交叉或变异算子的组合所定义.理论分析证明,算法以概率1收敛于全局最优解.对15个标准测试函数进行的测试显示,该算法能够找到其中几乎所有被测函数的最优解或好的次优解.与3个已有的算法相比,在评价次数相同时,该算法所求解的精度更高.同时,该算法的运行时间较短,甚至略短于同等设置下的标准遗传算法.此外,对参数的实验分析显示,该算法对参数不敏感,易于使用.     

基于多目标优化的控制器设计方法

多目标优化方法对所用的目标函数和约束要进行分析和改进, 为了多个目标的同时实现和分析本文研究了一种新的基于组织型 P 系统的多目标优化算法来设计 PID 控制器. 控制器参数被编码后按照与膜结构相关的规则进化, 组织型 P 系统具有独特的动态膜结构, 整个参数群体被动态膜结构划分成几个子群体计算降低了计算复杂性. 仿真结果表明所提算法收敛快, 解的精度高, 而且在 Pareto 前沿上均匀分布, 所得的控制器具有令人满意的控制性能. 实验结果显示新算法适于研究不同性能指标和调节参数之间的关系, 可以用于设计和评估不同的控制器.     

一种新的求解约束多目标优化问题的遗传算法

由于采用罚函数法将有约束多目标优化问题转化为无约束多目标优化问题会使求解不合理,因此,文章首先在无约束Pareto排序遗传算法的基础上,提出了一个简单、实用的能分别考虑目标函数和约束函数,而又可以避免采用罚函数的全新排序方法。接着,针对小生境技术在遗传后期依旧会出现遗传漂移现象和共享半径不易确定等缺陷,提出了一种易于实现的超量惩罚策略来替代小生境技术,用以改进种群的多样性。此外,还采用了Pareto解集过滤器、邻域变异和群体重组等策略对算法的寻优能力进行改进,并最终形成了一种求解有约束多目标优化问题的Pareto遗传算法(CMOPGA),还给出了具体的算法流程图。最后采用两个数值算例对算法的求解性能进行了测试。数值试验表明,采用CMOPGA可方便地求得问题的Pareto前沿,并能使求得的Pareto最优解集具有可靠、均布、多样等特点。     

解约束最优化问题的一个新的多目标进化算法

把约束函数作为目标函数,将约束优化问题转化为多目标规划问题。对这个多目标规划,根据带权极小极大策略构造了一个同进化代数有关的变适应值函数。利用广义球面坐标变换和均匀设计法来选择权重,使得由此权重确定的适应值函数能使种群中的容许解逐渐增加并且保持其多样性。用均匀设计法构造的带有自适应性的变异算子增强了算法的局部搜索能力。该方法能有效处理约束,特别是紧约束。计算机仿真显示了该方法是有效的。     

A Paradigm for Dynamic Coordination of Multiple Robots

In this paper, we present a paradigm for coordinating multiple robots in the execution of cooperative tasks. The basic idea in the paper is to assign to each robot in the team, a role that determines its actions during the cooperation. The robots dynamically assume and exchange roles in a synchronized manner in order to perform the task successfully, adapting to unexpected events in the environment. We model this mechanism using a hybrid systems framework and apply it in different cooperative tasks: cooperative manipulation and cooperative search and transportation. Simulations and real experiments demonstrating the effectiveness of the proposed paradigm are presented.     

求解多目标问题的Memetic免疫优化算法

将基于Pareto支配关系的局部下山算子和差分算子引入免疫多目标优化算法之中,提出了一种求解多目标问题的Memetic免疫优化算法(Memetic immune algorithm for multiobjective optimization,简称MIAMO).该算法利用种群中抗体在决策空间上的位置关系设计了两种有效的启发式局部搜索策略,提高了免疫多目标优化算法的求解效率.仿真实验结果表明,MIAMO与其他4种有效的多目标优化算法相比,不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且算法的收敛速度与免疫多目标优化算法相比明显加快.     

PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives

Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. However, the existing MOPSO designs generally adopt a notion to “estimate” a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. To address the issue, this paper proposes the integration of a dynamic population strategy within the multiple-swarm MOPSO. The proposed algorithm is named dynamic population multiple-swarm MOPSO. An additional feature, adaptive local archives, is designed to improve the diversity within each swarm. Performance metrics and benchmark test functions are used to examine the performance of the proposed algorithm compared with that of five selected MOPSOs and two selected multiobjective evolutionary algorithms. In addition, the computational cost of the proposed algorithm is quantified and compared with that of the selected MOPSOs. The proposed algorithm shows competitive results with improved diversity and convergence and demands less computational cost.      

Convergence Acceleration Operator for Multiobjective Optimization

A convergence acceleration operator (CAO) is described which enhances the search capability and the speed of convergence of the host multiobjective optimization algorithm. The operator acts directly in the objective space to suggest improvements to solutions obtained by a multiobjective evolutionary algorithm (MOEA). The suggested improved objective vectors are then mapped into the decision variable space and tested. This method improves upon prior work in a number of important respects, such as mapping technique and solution improvement. Further, the paper discusses implications for many-objective problems and studies the impact of the use of the CAO as the number of objectives increases. The CAO is incorporated with two leading MOEAs, the non-dominated sorting genetic algorithm and the strength Pareto evolutionary algorithm and tested. Results show that the hybridized algorithms consistently improve the speed of convergence of the original algorithm while maintaining the desired distribution of solutions. It is shown that the operator is a transferable component that can be hybridized with any MOEA.      

On Set-Based Multiobjective Optimization

Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only a few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general preference-independent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.