A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure.Most candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with diversity.Can we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean space.First,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones.Besides position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection pressure.Then,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased estimator.Based on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢframework.The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives.Experimental results demonstrate its superior competitiveness in solving MaOPs.

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently,which will ignore the correlation information existed in the data.Hence,if we can explore the correlation information hidden in the data,the classification result will be improved significantly.To this end,in this paper,a novel weighted supervised spare coding method is proposed to address the image classification problem.The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation.And then,it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.

A naive optimization method for multi-line systems with alternative machines

The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing systems.When the two problems are considered simultaneously,the resulting problem is much more complex than either of them.Obtaining sufficient training data for conventional data-based optimization approaches is difficult because of the high diversity of system structures.Consequently,optimization of multi-line systems with alternative machines requires a simple mechanism and must be minimally dependent on historical data.To define a general multi-line system with alternative machines,this study introduces the capability vector and matrix and the distribution vector and matrix.A naive optimization method is proposed in accordance with classic feedback control theory,and its key approaches are introduced.When a reasonable target value is provided,the proposed method can realize closed-loop optimization to the selected objective performance.Case studies are performed on a real 5/6-inch semiconductor wafer manufacturing facility and a simulated multi-line system constructed on the basis of the MiniFAB model.Results show that the proposed method can effectively and efficiently optimize various objective performance.The method demonstrates a potential for utilization in multi-objective optimization.

The Message from Editor for the Inaugural Issue

The studies of Management Science and Engineering emphasizes on both principle and methodology in solving problems,making decisions,and dealing with risks in complex management/business systems.Entering into 21st century,the scholars in this field face demanding research efforts resulting from the collision of technology advancement,social changes,economic transformation,and common challenges(like aging,climate change,or terrorism)in the globe.To address the new era challenges,as well as opportunities,to management and business,new perspectives and methodologies,such as data-driven approaches,computing or agent-based modeling/simulating of complex management and business systems,arise rapidly and bring together the collaborative efforts from multidiscipline for the innovative solutions,though model-driven approaches,such as operation research and optimization,still remain dominant in the field and addressed by the existing publications.

IMPULSIVE CONTROL IN MICROORGANISMS CONTINUOUS FERMENTATION

This work presents the nonlinear dynamical system of continuous fermentation from glycerol to 1,3-propanediol. The impulsive control scheme of continuous culture is intro- duced. By employing impulsive control, Lyapunov＇s method and comparison technique, sufficient conditions are established for the asymptotical stability and synchronization of the dynamical system of continuous fermentation. The upper bound of the impulse interval is also estimated. An example will illustrate the effectiveness of the results in Sec. 4.