混合变异克隆选择多目标优化算法

研究多目标优化问题,针对提高算法的快速性,提出一种混合变异克隆选择多目标优化算法.进化在三个抗体群中进行,不同的抗体群采用不同的变异算子,并通过外部记忆抗体群的更新,来保留进化的最优抗体,避免算法进化后期出现退化现象.算法采用的三种变异算子:高频大尺度高斯变异算子带有振荡性质,能够对Pareto最优解区域进行勘探,单基因小尺度衰减的高斯变异算子能够使优化结果逼近Pareto最优解,均匀变异算子使算法具有局部逃逸能力,能够保证解的多样性.将算法和经典的NSGA -Ⅱ、ε- MOEA算法以及单一变异的多目标克隆选择算法(MCSA)进行性能比较,结果证明新算法具有较好的快速搜索性能和鲁棒性.     

用于机动目标跟踪的多模型高斯混合概率假设密度滤波器

提出一种多模型高斯混合概率假设密度(PHD)滤波器的实现方法.该算法使用多模型方法对高斯混合PHD滤波器中存在目标对应的高斯分量进行预测及更新,使用融合估计后的估计值描述机动目标PHD分布的高斯分量.该算法具有PHD滤波器和多模型方法的优点,可以用来处理目标数未知时的机动目标跟踪问题.该算法与单模型高斯混合PHD滤波器相比,可提高滤波器对目标发生机动时的跟踪精度;与已有的多模型PHD滤波器相比,节省计算时间30％以上.     

基于高斯变异的生物地理学优化模型

生物地理学优化是一种新型群体智能算法,具有较好的应用前景.针对算法中两大基本算子之一的变异算子进行研究,为了进一步提高优化模型的精度,给出关于高斯变异的生物地理学优化模型.同时介绍了算法的基本原理,重点分析了算法中的变异策略,采用多个测试函数进行仿真.仿真结果表明,在相同的迁移模型下,不同的变异策略对算法优化性能有较大影响,高斯变异策略的优化性能优于随机变异策略.实验还表明栖息地数量对于算法的优化能力也有较大的影响.     

基于CCEA和改进郭涛算法的函数优化问题求解

在合作式协同演化(CCEA)的基础上,引入了使用高斯变异算子的郭涛算法,设计了一种求解函数优化问题的高效混合演化算法.通过求解复杂高维的函数优化问题的多个实例,并将该算法和CCEA与基本郭涛算法结合的方法--CCGT进行了对比.实验结果表明,新算法是高效的,其结果优于现存文献中的其它模型,但不及CCGT优秀.证明在该算法中引入的高斯变异算子的作用不大.     

基于混合高斯模型的Mean-Shift模板更新算法

针对基于Mean-Shift目标跟踪算法中遇到的不能对模板进行实时更新的问题,提出一个基于混合高斯背景建模的目标模板更新算法.该算法将目标视为背景,对目标中的每一个像素点利用三个高斯函数对它进行建模,利用每次Mean-Shift跟踪到的目标区域来对先前建立的混合高斯模型进行实时更新,将混合高斯模型得到的目标模板作为下一帧跟踪的目标模板.该算法较好地解决了基于Mean-Shift算法的模板更新问题,实验证明该算法是有效的.     

一种多特征联合的运动目标检测算法

高斯混合模型已经成为对视频利用背景减除法进行运动目标检测的最多的一种背景建模模型,也成为一种标准模型。首先对高斯混合模型的理论框架及其性能进行了分析,分析了高斯混合模型仍需要解决的问题,并提出一种高斯混合模型联合多特征的运动目标检测算法,实验表明该算法具有较好的目标检测效果以及环境自适应性。     

基于局部搜索与混合多样性策略的多目标粒子群算法

为了提高算法的收敛性与非支配解集的多样性,提出一种基于局部搜索与混合多样性策略的多目标粒子群算法（LH-MOPSO）.该算法使用增广Lagrange乘子法对非支配解进行局部搜索以快速接近Pareto最优解;利用基于改进的Maximin适应值函数与拥挤距离的混合多样性策略对非支配解集进行维护以保留解的多样性,同时引入高斯变异算子以避免算法早熟收敛;最后针对多目标约束优化问题,给出一种有效的约束处理方法.实验研究表明该算法具有良好的优化性能.     

基于高斯混合模型的活动轮廓模型脑MRI分割

传统的活动轮廓模型用于图像分割往往基于目标的边界信息,在图像含有强噪音或目标具有弱边界时很难得到真实解.引入高斯混合模型构造新的约束项,在新的约束项作用下模型可以减少噪音的影响,并防止从弱边界泄漏.高斯混合模型求解通常使用Expectation-maximization(EM)算法,该算法是局部优化算法,且对初值敏感.因此引入粒子群算法,并提出一种改进的算法,利用该算法的全局优化性求解高斯混合模型的参数,以提高参数精度.对脑核磁共振图像(MRI)分割实验表明该模型具有较好的分割效果.     

生物地理信息优化算法中迁移算子的改进

原生物地理信息优化算法主要通过迁移算子与变异算子实现群体的进化, 常被应用于求解单目标优化问题。 如果将原有的进化算子直接用于求解连续多目标优化问题,会严重影响群体的多样性。文中将原迁移算子进行改进, 引入扰动因子, 增强群体的多样性。并以此为基础,提出基于生物地理信息的多目标进化算法(BBMOEA)。 通过与原有迁移算子下的算法比较及各类型测试函数的实验, 结果验证改进迁移算子对于求解多目标优化问题是有效可行的。同时将BBMOEA与经典算法SPEA2和NSGA-Ⅱ进行比较, 结果表明BBMOEA所得Pareto解集在收敛的同时,具有较均匀的分布性。     

基于自适应分解的多任务协作型昂贵多目标优化算法

现实世界的工程优化问题通常需要同时优化多个冲突的目标,且这些目标函数的评估由于依赖仿真、物理实验而十分昂贵,这类问题被称为昂贵多目标优化问题.使用机器学习方法建立代理模型用于估计候选解的目标函数值是求解此类问题的一种有效手段.高斯代理模型适用于训练样本数较少的中小规模问题,且能提供评估的不确定性,因此常作为代理模型被应用于昂贵优化.分解是处理多目标优化问题的一种有效手段.一个多目标优化问题可被分解为多个单目标优化子问题,且多个子问题可被进一步划分为代理模型学习的一个目标任务.现有基于分解的昂贵多目标优化算法大多将固定数量的子问题静态地划分到同一任务,从而构造多个固定任务并对其建立多任务高斯代理模型进行求解.这未能充分利用数据的相关信息动态反映出任务间的相关性,限制了多任务高斯过程模型的预测精度以及优化算法的最终性能.为此,本文提出了一种自适应多任务多种群协作搜索算法(AMMCS).AMMCS使用相似性指标实时度量已评估的解集,获得子问题间的相关性,从而自适应地划分任务,提升多任务模型的预测质量.此外,AMMCS使用一个解集(种群)优化一个任务,并通过多种群的协作搜索实现多任务高斯模型的批量优化,提高了采样效率,提升了算法的收敛效率.通过AMMCS与六个代理辅助进化算法进行多组实验对比和分析,显示了AMMCS具有良好的性能.我们同时也设计实验验证了算法中自适应分解以及多种群协作搜索的有效性.     

Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation

In the last two decades, multiobjective optimization has become main stream and various multiobjective evolutionary algorithms (MOEAs) have been suggested in the field of evolutionary computing (EC) for solving hard combinatorial and continuous multiobjective optimization problems. Most MOEAs employ single evolutionary operators such as crossover, mutation and selection for population evolution. In this paper, we suggest a multiobjective evolutionary algorithm based on multimethods (MMTD) with dynamic resource allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm employs two well known population based stochastic algorithms namely MOEA/D and NSGA-II as constituent algorithms for population evolution with a dynamic resource allocation scheme. We have examined the performance of the proposed MMTD on two different MOPs test suites: the widely used ZDT problems and the recently formulated test instances for the special session on MOEAs competition of the 2009 IEEE congress on evolutionary computation (CEC’09). Experimental results obtained by the suggested MMTD are more promising than those of some state-of-the-art MOEAs in terms of the inverted generational distance (IGD)-metric on most test problems.     

A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D.     

A novel adaptive control strategy for decomposition-based multiobjective algorithm

Recently, evolutionary algorithm based on decomposition (MOEA/D) has been found to be very effective and efficient for solving complicated multiobjective optimization problems (MOPs). However, the selected differential evolution (DE) strategies and their parameter settings impact a lot on the performance of MOEA/D when tackling various kinds of MOPs. Therefore, in this paper, a novel adaptive control strategy is designed for a recently proposed MOEA/D with stable matching model, in which multiple DE strategies coupled with the parameter settings are adaptively conducted at different evolutionary stages and thus their advantages can be combined to further enhance the performance. By exploiting the historically successful experience, an execution probability is learned for each DE strategy to perform adaptive adjustment on the candidate solutions. The proposed adaptive strategies on operator selection and parameter settings are aimed at improving both of the convergence speed and population diversity, which are validated by our numerous experiments. When compared with several variants of MOEA/D such as MOEA/D, MOEA/D-DE, MOEA/D-DE+PSO, ENS-MOEA/D, MOEA/D-FRRMAB and MOEA/D-STM, our algorithm performs better on most of test problems.     

Decomposing the user-preference in multiobjective optimization

Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization; however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches, g-dominance and r-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.     

MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.     

Solving multiobjective problems using cat swarm optimization

This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs).     

Multiobjective evolutionary algorithms for electric power dispatch problem

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.     

基于分解的演化多目标优化算法综述

基于分解的演化多目标优化算法(MOEA/D)的基本思想是将一个多目标优化问题转化成一系列子问题(单目标或者多目标)来进行优化求解.自2007年提出以来, MOEA/D受到了国内外学者的广泛关注,已经成为最具代表性的演化多目标优化算法之一.总结过去13年中关于MOEA/D的一些研究进展,具体内容包括:(1)关于MOEA/D的算法改进;(2) MOEA/D在超多目标优化问题及约束优化问题上的研究;(3) MOEA/D在一些实际问题上的应用.然后,实验对比几个具有代表性的MOEA/D改进算法.最后,指出一些MOEA/D未来的研究方向.     

Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test instances with arbitrary prescribed PS shapes, which could be used for studying the ability of multiobjective evolutionary algorithms for dealing with complicated PS shapes. It also proposes a new version of MOEA/D based on differential evolution (DE), i.e., MOEA/D-DE, and compares the proposed algorithm with NSGA-II with the same reproduction operators on the test instances introduced in this paper. The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.     

MCEDA: A novel many-objective optimization approach based on model and clustering

To solve many-objective optimization problems (MaOPs) by evolutionary algorithms (EAs), the maintenance of convergence and diversity is essential and difficult. Improved multi-objective optimization evolutionary algorithms (MOEAs), usually based on the genetic algorithm (GA), have been applied to MaOPs, which use the crossover and mutation operators of GAs to generate new solutions. In this paper, a new approach, based on decomposition and the MOEA/D framework, is proposed: model and clustering based estimation of distribution algorithm (MCEDA). MOEA/D means the multi-objective evolutionary algorithm based on decomposition. The proposed MCEDA is a new estimation of distribution algorithm (EDA) framework, which is intended to extend the application of estimation of distribution algorithm to MaOPs. MCEDA was implemented by two similar algorithm, MCEDA/B (based on bits model) and MCEDA/RM (based on regular model) to deal with MaOPs. In MCEDA, the problem is decomposed into several subproblems. For each subproblem, clustering algorithm is applied to divide the population into several subgroups. On each subgroup, an estimation model is created to generate the new population. In this work, two kinds of models are adopted, the new proposed bits model and the regular model used in RM-MEDA (a regularity model based multi-objective estimation of distribution algorithm). The non-dominated selection operator is applied to improve convergence. The proposed algorithms have been tested on the benchmark test suite for evolutionary algorithms (DTLZ). The comparison with several state-of-the-art algorithms indicates that the proposed MCEDA is a competitive and promising approach.