基于多目标进化算法的MOEA/D权重向量产生方法

在进化多目标优化研究领域,多目标优化是指对含有2个及以上目标的多目标问题的同时优化,其在近些年来受到越来越多的关注。随着MOEA/D的提出,基于聚合的多目标进化算法得到越来越多的研究,对MOEA/D算法的改进已有较多成果,但是很少有成果研究MOEA/D中权重的产生方法。提出一种使用多目标进化算法产生任意多个均匀分布的权重向量的方法,将其应用到MOEA/D,MSOPS和NSGA-III中,对这3个经典的基于聚合的多目标进化算法进行系统的比较研究。通过该类算法在DTLZ测试集、多目标旅行商问题MOTSP上的优化结果来分别研究该类算法在连续性问题、组合优化问题上的优化能力,以及使用矩形测试问题使得多目标进化算法的优化结果在决策空间可视化。实验结果表明,没有一个算法能适用于所有特性的问题。然而,MOEA/D采用不同聚合函数的两个算法MOEA/D_Tchebycheff和MOEA/D_PBI在多数情况下的性能比MSOPS和NSGA-III更好。     

Physical programming for preference driven evolutionary multi-objective optimization

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.     

社会认识优化在非线性规划问题中的应用

社会认识优化(Society Cognitive Optimization,SCO)是一种基于社会认知理论提出的模拟人类社会的演化算法.社会认识优化是通过竞争选择和领域搜索来模拟社会认知理论中的社会学习能力,用代理来代表社会中的人,用知识库来代表社会中的知识,通过代理与知识库之间不断的交互来模拟人类的社会学习过程,从而达到优化学习的目的.命题逻辑中合取范式的可满足性(Satisfyability,SAT)问题是当代理论计算机科学的核心问题,是一典型的NP完全问题.可满足性问题的有效解决有着重要的理论意义和实际应用价值.文中将社会认识优化算法应用于求解可满足性问题,得到了比较满意的结果.     

求解多目标优化问题的新遗传算法

给出了求解多目标优化问题的一种新解法。定义了多目标优化问题的非劣方向,设计了方向杂交算子和简单的变异算子。标准算例的计算机仿真结果表明,新算法可以快速地找到一组范围广、分布均匀且数量充足的Pareto最优解。     

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.     

基于改进鱼群算法的多无人机任务分配研究

多无人机协同任务分配问题是多无人机协同控制的关键,为解决单目标函数构建的任务分配模型不能满足决策者对战场环境大量信息的需求,以最大航程和最长任务执行时间作为多无人机任务分配的两个目标函数,依据多目标优化理论,建立了协同任务分配多目标优化模型.并采用了一种借鉴遗传算法中的变异思想的改进鱼群算法进行求解,得到多无人机任务分配的多目标最优解集,然后根据决策者的偏好选择最佳任务分配方案.最后将上述算法应用于多无人机协同任务分配中并进行了仿真,仿真结果验证了改进鱼群算法的收敛性及有效性,为多无人机协同任务分配优化提供了参考依据.     

基于AMOWOA的区域综合能源系统运行优化调度

目前, 智能优化算法已广泛应用于工程优化中, 在当前多能耦合与互补的能源发展趋势下, 仅考虑系统经济指标的单目标优化模式已经不再适用于目前区域综合能源系统(Integrated energy system, IES)的运行优化调度, 需要研究一种多目标运行策略来解决区域综合能源系统的运行优化调度问题. 首先综合考虑经济与能源利用两个指标并结合商业住宅区域的特性, 以系统日运行收益和一次能源利用率为优化目标构建商业住宅区域综合能源系统多目标运行优化调度模型. 其次由于传统多目标智能优化算法缺乏一种最优解综合评价方法, 基于非支配排序以及拥挤度计算的多目标算法框架, 提出一种利用模糊一致矩阵选取全局最优解的多目标鲸鱼优化算法(A multi-objective whale optimization algorithm, AMOWOA), 并将提出算法对商住区域综合能源系统多目标运行优化调度模型进行求解. 最后以华东某商业住宅区域综合能源系统为例进行仿真, 验证了该方法的有效性和可行性.     

Artificial immune multi-objective SAR image segmentation with fused complementary features

Artificial immune systems (AIS) are the computational systems inspired by the principles and processes of the vertebrate immune system. AIS-based algorithms typically mimic the human immune system’s characteristics of learning and adaptability to solve some complicated problems. Here, an artificial immune multi-objective optimization framework is formulated and applied to synthetic aperture radar (SAR) image segmentation. The important innovations of the framework are listed as follows: (1) an efficient and robust immune, multi-objective optimization algorithm is proposed, which has the features of adaptive rank clones and diversity maintenance by K-nearest-neighbor list; (2) besides, two conflicting, fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and (3) moreover, an effective, fused feature set for texture representation and discrimination is constructed and researched, which utilizes both the Gabor filter’s ability to precisely extract texture features in low- and mid-frequency components and the gray level co-occurrence probability’s (GLCP) ability to measure information in high-frequency. Two experiments with synthetic texture images and SAR images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms: fuzzy C-means (FCM), single-objective genetic algorithm (SOGA), self-organizing map (SOM), wavelet-domain hidden Markov models (HMTseg), and spectral clustering ensemble (SCE). Experimental results show the proposed framework has obtained the better performance in segmenting SAR images than other five algorithms and behaves insensitive to the speckle noise.     

Solution of multiobjective optimization problems: coevolutionary algorithm based on evolutionary game theory

When attempting to solve multiobjective optimization problems (MOPs) using evolutionary algorithms, the Pareto genetic algorithm (GA) has now become a standard of sorts. After its introduction, this approach was further developed and led to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to keep diversity. On the other hand, the scheme for solving MOPs presented by Nash introduced the notion of Nash equilibrium and aimed at solving MOPs that originated from evolutionary game theory and economics. Since the concept of Nash Equilibrium was introduced, game theorists have attempted to formalize aspects of the evolutionary equilibrium. Nash genetic algorithm (Nash GA) is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash equilibrium through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an evolutionary stable strategy (ESS). In this article, we find the ESS as a solution of MOPs using a coevolutionary algorithm based on evolutionary game theory. By applying newly designed coevolutionary algorithms to several MOPs, we can confirm that evolutionary game theory can be embodied by the coevolutionary algorithm and this coevolutionary algorithm can find optimal equilibrium points as solutions for an MOP. We also show the optimization performance of the co-evolutionary algorithm based on evolutionary game theory by applying this model to several MOPs and comparing the solutions with those of previous evolutionary optimization models. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

A new look at ESO and BESO optimization methods

The “hard-kill” optimization methods such as evolutionary structural optimization (ESO) and bidirectional evolutionary structural optimization (BESO) may result in a nonoptimal design (Zhou and Rozvany in Struct Multidisc Optim 21:80–83, 2001) when these methods are implemented and used inadequately. This note further examines this important problem and shows that failure of ESO may occur when a prescribed boundary support is broken for a statically indeterminate structure. When a boundary support is broken, the structural system could be completely changed from the one originally defined in the initial design and even BESO would not be able to rectify the nonoptimal design. To avoid this problem, it is imperative that the prescribed boundary conditions for the structure be checked and maintained at each iteration during the optimization process. Several simple procedures for solving this problem are suggested. The benchmark problem proposed by Zhou and Rozvany (Struct Multidisc Optim 21:80–83, 2001) is revisited, and it is shown that the highly nonoptimal design can be easily avoided.     

基于模糊优化的物流配送路径(MLRP)问题研究

研究采用嵌入模糊决策规则的遗传算法（即模糊优化方法）求解物流配送多目标定位-运输路线安排问题（MLRP）,重点考虑了时间和运输成本两个目标的MLRP的求解方法.该算法分成3个阶段,首先利用遗传算法对初始种群搜索选择优化配送路径;然后应用配送网络调度算法综合评价来确定配送路径中的关键路径和非关键路径;最后根据模糊决策规则计算其各个调度相应的指标,并对已挑选出来的染色体中的某些位基因进行调整,以提高算法的收敛性.计算机仿真结果证明了将此混合算法用于求解中、小规模物流配送问题的有效性.     

An automatic approach to reaching consensus in multiple attribute group decision making

In this work, we consider the problem of consensus of multiple attribute group decision making, and develop an automatic approach to reaching consensus among group opinions. In the process of group decision making, each expert provides his/her preferences over the alternatives with respect to each attribute, and constructs an individual decision matrix. The developed approach first aggregates these individual decision matrices into a group decision matrix by using the additive weighted aggregation (AWA) operator, and then establishes a convergent iterative algorithm to gain a consentaneous group decision matrix. Then based on the consentaneous group decision matrix, the approach utilizes the AWA operator to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Finally, we detailedly expound the implementation process of the approach with a practical example.     

混沌云模型多目标布谷鸟搜索算法

针对多目标布谷鸟搜索算法（MOCS）迭代后期寻优速度慢,并且容易造成局部最优等缺点,提出一种混沌云模型多目标布谷鸟搜索算法（CCMMOCS）。首先在进化过程中通过混沌理论对一般的布谷鸟巢位置在全局中寻求优化,以防落入局部最优;然后利用云模型对较好的布谷鸟巢位置局部优化来提高精度;最后将两种方法对比得到相对更好的解作为最优值以完成优化。对比误差估计值及多样性指标,由5个常用多目标测试函数仿真结果可知,CCMMOCS比传统多目标布谷鸟搜索算法、多目标粒子群算法（MOPSO）及多目标遗传（NSGA-Ⅱ）算法性能更好,Pareto前沿更接近理想曲线,分布也更均匀。     

An improved group search optimizer with operation of quantum-behaved swarm and its application

Group search optimizer (GSO) is a novel swarm intelligent (SI) algorithm for continuous optimization problem. The framework of the algorithm is mainly based on the producer-scrounger (PS) model. Comparing with ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms, GSO emphasizes more on imitating searching behavior of animals. In standard GSO algorithm, more than 80% individuals are chosen as scroungers, and the producer is the one and only destination of them. When the producer cannot found a better position than the old one in some successive iterations, the scroungers will almost move to the same place, the group might be trapped into local optima though a small quantity of rangers are used to improve the diversity of it. To improve the convergence performance of GSO, an improved GSO optimizer with quantum-behaved operator for scroungers according to a certain probability is presented in the paper. In the method, the scroungers are divided into two parts, the scroungers in the first part update their positions with the operators of QPSO, and the remainders keep searching for opportunities to join the resources found by the producer. The operators of QPSO are utilized to improve the diversity of population for GSO. The improved GSO algorithm (IGSO) is tested on several benchmark functions and applied to train single multiplicative neuron model. The results of the experiments indicate that IGSO is competitive to some other EAs.