基于变异算子和邻域值自适应的MOEA/D算法

基于分解的多目标进化算法（MOEA/D）在解决多目标问题时，具有简单有效的特点。但多数MOEA/D采用固定的控制参数，导致全局搜索能力差，难以平衡收敛性和多样性。针对以上问题提出一种基于变异算子和邻域值自适应的多目标优化算法。该算法根据种群中个体适应度值的分散或集中程度进行判断，并据此对变异算子进行自适应的调节，从而增强算法的全局搜索能力；根据进化所处的阶段以及个体适应度值的集中程度，自适应地调节邻域值大小，保证每个个体在不同的进化代数都有一个邻域值大小；在子问题邻域中，统计子问题对应个体的被支配数，通过判断被支配数是否超过设定的上限，来决定是否将Pareto支配关系也作为邻域内判断个体好坏的准则之一。将提出的算法与传统的MOEA/D在标准测试问题上进行对比。实验结果表明，提出的算法求得的解集具有更好的收敛性和多样性，在求解性能上具有一定的优势。     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Sustainable optimization of waste management network over extended planning time horizon

This study proposes a multiperiod mixed integer linear programming model for the management of a single municipal solid waste (MSW) treatment plant with sustainability as the objective. Discrete and continuous variables define the capacity selections for diverse MSW technologies, and the operation of the MSW network, respectively. The economic target is considered to maximize the net present value. The environmental impact is the minimization of a normalized environmental objective function (NEOF). The social target is the maximization of jobs. An interesting feature about the research work is the requirement of biodrying technologies for MSW moisture content control. Due to the conflicted nature among the sustainability components, a multiobjective optimization (MO) is carried out to find the Pareto optimal solutions. The MO results show that the Pareto optimal solutions vary around profit range of $4.9–8.5 billion, NEOF impact range of 3.2–3.6 units, and social benefit range of 2700–4828 jobs.     

Biofuel supply chain optimal design considering economic,environmental, and societal aspects towards sustainability

Biofuel supply chain design plays a critical role in facilitating the large‐scale substitution of biofuel for traditional fossil fuels with a cost‐effective and environmentally friendly manner towards sustainability. This paper proposes a multiobjective optimization model for a 4‐layer biofuel supply chain network using mixed integer nonlinear programming while considering the benefits from economic, environmental, and societal aspects. The model can be used either to optimize an existing biofuel supply chain network or to guide the construction of a new biofuel supply chain network. The profit, the greenhouse gas emissions in transportation, and the market share of biofuel were set as targets for optimization. The selection of the participators at each layer, and the amount of the material flow between each pair of selected supplier and customer located at two adjacent layers were modeled as decision variables. The conventional weighted aggregation method was used to unify 3 objectives after normalization. Particle swarm optimization was used to solve this high‐dimension multiobjective problem to obtain a near optimal solution. A numerical case study based on the state of Missouri in the United States was implemented to verify the effectiveness of the proposed model. The results of the case study illustrate that the benefits in terms of transportation emission, profit, and market share can be achieved simultaneously. Using the equal weights configuration in conventional weighted aggregation as an example, a 21% reduction of the transportation emission, a 33% increase of the profit, and a 2% augmentation of the market share were achieved compared to the benchmark scenario.     

Multiemotional product color design using gray theory and nondominated sorting genetic algorithm-III

When users select products, they consider the emotional experience resulting from the color of the product. However, the emotional demands of users for product color are multidimensional and diverse. It is very important yet difficult to accurately grasp multiemotional image requirements and effectively convert them into design elements. Therefore, multiemotional product color design (MEPCD) has become a very important and challenging research topic. In this article, a novel MEPCD system using gray theory (GT) and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to effectively solve the MEPCD problem. First, the image perception spaces of users, which exist in different emotional dimensions, were collected using factor analysis and the semantic differential technique. Then, GT was used to establish a multidimensional emotional product color image evaluation model. Finally, NSGA-III was used to optimize and design a multiemotional color scheme for a product. Furthermore, according to actual conditions, an MEPCD system was established based on the proposed method. The design case study shows that the method and design system proposed in this article have a certain range of applicability and can effectively improve the practicality of MEPCD.     

计及UPFC的电力系统多阶段多目标无功优化算法

统一潮流控制器(UPFC)并联侧连续的无功调节能力为电力系统无功优化提供了新的控制手段。基于此,首先建立适用于新型UPFC拓扑的UPFC稳态模型;然后考虑无功设备动作次数的约束,明确多目标无功优化问题的目标函数和约束条件,建立计及UPFC的多目标无功优化模型;接着,提出一种多阶段方法对其进行求解,其中,第一阶段将原问题进行松弛并采取归一化的方法统一多个目标的量纲,第二阶段基于规格化平面约束法获取松弛问题的Pareto最优候选解集,并给出折衷解的选取方法,第三阶段基于三角罚函数法对折衷解中的整数变量进行归整,获取原问题的最优折衷整数解。最后,对南京西环网实际等值系统进行算例测试,验证了算法的有效性及UPFC在无功优化问题中的应用前景。     

A multiobjective thermodynamic optimization of a nanoscale Stirling engine operated with Maxwell‐Boltzmann gas

Recent developments in nanotechnology provided an opportunity to solve many complex problems in the field of energy. Performance investigation of the nanoscale thermal cycles can prove crucial in the development of efficient and less polluting energy system. Due to the influence of boundary phenomenon and quantum degeneracy effects, a nanoscale engine performs according to statistical quantum thermodynamics instead of classical thermodynamics. In this study, a nanoscale Stirling engine operating on an ideal Maxwell‐Boltzmann gas is investigated for multiobjective optimization. Optimization problem of Stirling cycle is formed considering the thermal efficiency, ecological coefficient of performance and entropy generation. An application example of a nanoscale Stirling engine is presented and solved using Heat Transfer Search algorithm. Maxwell‐Boltzmann gas restricted in a finite domain is studied and the effect of different parameters, such as surface area ratio, volume ratio, and temperature ratio of the domain, is investigated. Sensitivity analysis is carried out to identify the effect of design variables on the performance parameters. Further, influence of the source temperature and the number of particles of working fluid on the objective functions is studied and presented.     

基于个体邻域的改进NSGA-Ⅱ算法

带有精英策略的非支配排序遗传算法（NSGA-II）是在NSGA的基础之上，提出拥挤度和拥挤度比较算子，代替了需要指定共享半径的适应度共享策略，是解决多目标优化问题的经典算法之一。但是NSGA-II算法在保持种群多样性时采取的拥挤距离排挤机制有着pareto前沿分布不均匀的缺陷，因此，提出一种基于个体邻域的改进NSGA-II算法SN-NSGA2。SN-NSGA2将密度聚类算法DBSCAN中邻域的思想应用到排挤机制中去，提出一种个体邻域的构建方法，采用相应的淘汰策略去除个体邻域中的其他邻居个体。实验结果表明相对于NSGA-II算法来说，新算法求出的pareto解集有着更好的分布性以及良好的收敛性。     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.