An efficient multiobjective differential evolution algorithm for engineering design

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.     

Robust optimization of 2D airfoils driven by full Navier-Stokes computations

A new approach to the constrained design of aerodynamic shapes is suggested. The approach employs Genetic Algorithms (GAs) as an optimization tool in combination with a Reduced-Order Models (ROM) method based on linked local data bases obtained by full Navier-Stokes computations. The important features of the approach include: (1) a new strategy for efficient handling of non-linear constraints in the framework of GAs (2) scanning of the optimization search space by a combination of full Navier-Stokes computations with the ROM method (3) multilevel parallelization of the whole computational framework.The method was applied to the problem of one-point transonic profile optimization with non-linear constraints. The results demonstrated that the approach combines high accuracy of optimization (based on full Navier-Stokes computations) and efficient handling of various non-linear constraints with high computational efficiency and robustness. A significant computational time-saving (in comparison with optimization tools fully based on Navier-Stokes computations) allowed the method to be used in a demanding engineering environment.     

A model-based sliding mode control methodology applied to the HDA-plant

A sliding mode control methodology using output information is demonstrated in application to the HDA-plant, a plant for production of benzene. This process is a highly integrated, non-linear large scale process with non-minimum phase and relative degree zero characteristics. The non-linear control law is designed on the basis of a linear observer-based control system. The non-linear control law uses the states of the linear observer. The performance in the sliding mode is determined by a linear stable sub-manifold of the linear closed loop control system chosen via a robust pole selection scheme. The sliding mode control is optimized to operate in a wide operating region.     

<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> Control Design Using Dynamic Neural Networks

In this paper, a new approach is investigated for adaptive dynamic neural network-based H _∞ control, which is designed for a class of non-linear systems with unknown uncertainties. Currently, non-linear systems with unknown uncertainties are commonly used to efficiently and accurately express the real practical control process. Therefore, it is of critical importance but a great challenge and still at its early age to design a stable and robust controller for such a process. In the proposed research, dynamic neural networks were constructed to precisely approximate the non-linear system with unknown uncertainties first, a non-linear state feedback H _∞ control law was designed next, then an adaptive weighting adjustment mechanism for dynamic neural networks was developed to achieve H _∞ regulation performance, and last a recurrent neural network was employed as a neuro-solver to efficiently and numerically solve the standard LMI problem so as to obtain the appropriate control gains. Finally, case studies further verify the feasibility and efficiency of the proposed research.     

离散非线性规划问题的改进遗传算法

针对实际离散非线性规划问题,分析了离散与连续变量优化问题和求解方法的不同及特性．根据离散变量与遗传算法的特点,将单纯形搜索与算术交叉思想相结合,提出离散单纯形交叉算子以提高遗传算法的局部寻优能力,将种群逐步向离散极值点进行引导,实现算法的快速离散寻优．同时,设计了离散变异算子,使遗传算子真正在离散空同中进行搜索．基于梯度下降思想提出离散修复算子,提高算法对非线性约束的处理能力．实际离散非线性规划问题的应用研究验证了方法的有效性．     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Robust maximum entropy clustering algorithm with its labeling for outliers

In this paper, a novel robust maximum entropy clustering algorithm RMEC, as the improved version of the maximum entropy algorithm MEC [2–4], is presented to overcome MEC's drawbacks: very sensitive to outliers and uneasy to label them. Algorithm RMEC incorporates Vapnik's ɛ – insensitive loss function and the new concept of weight factors into its objective function and consequently, its new update rules are derived according to the Lagrangian optimization theory. Compared with algorithm MEC, the main contributions of algorithm RMEC exit in its much better robustness to outliers and the fact that it can effectively label outliers in the dataset using the obtained weight factors. Our experimental results demonstrate its superior performance in enhancing the robustness and labeling outliers in the dataset.     

Interactive authoring of multimedia documents in a constraint-based authoring system

As multimedia applications spread widely, it is crucial for programming and design support systems to handle “time” in multimedia documents effectively and flexibly. This paper presents a set of interactive system support tools for designing and maintaining the temporal behavior of multimedia documents. The tool set provides mechanisms for anomaly detection, temporal query processing, and interactive scheduling. It is based on a fast incremental constraint solver we have developed, which can be adapted by any constraint-based system. The incremental constraint solver provides immediate feedback to the user, supporting a highly interactive design process. Combined with existing optimal layout generation mechanisms proposed in the literature, our tools effectively utilize the flexibility provided by constraint-based systems.     

An adaptive penalty based covariance matrix adaptation–evolution strategy

Although most of unconstrained optimization problems with moderate to high dimensions can be easily handled with Evolutionary Computation (EC) techniques, constraint optimization problems (COPs) with inequality and equality constraints are very hard to deal with. Despite the fact that only equality constraints can be used to eliminate a certain variable, both types of constraints implicitly enforce a relation between problem variables. Most conventional constraint handling methods in EC do not consider the correlations between problem variables imposed by the problem constraints. This paper relies on the idea that a proper genetic operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. With this in mind, we employ a (μ+λ)-Evolution Strategy with a simplified variant of Covariance Matrix Adaptation based mutation operator along an adaptive weight adjustment scheme. The proposed algorithm is tested on two test sets. The outperformance of the algorithm is significant on the first benchmark when compared with five conventional methods. The results on the second test set show that algorithm is highly competitive when benchmarked with three state-of-art algorithms. The main drawback of the algorithm is its slightly lower speed of convergence for problems with high dimension and/or large search domain.     

Structural topology design optimization using Genetic Algorithms with a bit-array representation

In this paper, a bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is implemented. The importance of structural connectivity in a design is further emphasized by considering the total number of connected objects of each individual explicitly in an equality constraint function. To evaluate the constrained objective function, Deb’s constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain. An identical initialization method is also proposed to improve the GA performance in dealing with problems with long narrow design domains. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly.     

Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems

This research discusses the application of a mixed-integer-binary small-population-based evolutionary particle swarm optimization to the problem of optimal power flow, where the optimization problem has been formulated taking into account four decision variables simultaneously: active power (continuous), voltage generator (continuous), tap position on transformers (integer) and shunt devices (binary). The constraint handling technique used in the algorithm is based on a strategy to generate and keep the decision variables in feasible space through the heuristic operators. The heuristic operators are applied in the active power stage and the reactive power stage sequentially. Firstly, the heuristic operator for the power balance is computed in order to maintain the power balance constraint through a re-dispatch of the thermal units. Secondly, the heuristic operators for the limit of active power flows and the bus voltage constraint at each generator bus are executed through the sensitivity factors. The advantage of our approach is that the algorithm focuses the search of the decision variables on the feasible solution space, obtaining a better cost in the objective function. Such operators not only improve the quality of the final solutions but also significantly improve the convergence of the search process. The methodology is verified in several electric power systems.     

On an alternative approach to stress constraints relaxation in topology optimization

The paper deals with the imposition of local stress constraints in topology optimization. The aim of the work is to analyze the performances of an alternative methodology to the ε-relaxation introduced in Cheng and Guo (Struct Optim 13:258–266, 1997), which handles the well-known stress singularity problem. The proposed methodology consists in introducing, in the SIMP law used to apply stress constraints, suitable penalty exponents that are different from those that interpolate stiffness parameters. The approach is similar to the classical one because its main effect is to produce a relaxation of the stress constraints, but it is different in terms of convergence features. The technique is compared with the classical one in the context of stress-constrained minimum-weight topology optimization. Firstly, the problem is studied in a modified truss design framework, where the arising of the singularity phenomenon can be easily shown analytically. Afterwards, the analysis is extended to its natural context of topology bidimensional problems.     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Stacking sequence optimization and blending design of laminated composite structures

The stacking sequence optimization problem for multi-region composite structures is studied in this work by considering both blending and design constraints. Starting from an initial stacking sequence design, unnecessary plies can be removed from this initial design and layer thicknesses of necessary plies are optimally determined. The existence of each ply is represented with discrete 0/1 variables and ply thicknesses are treated as continuous variables. A first-level approximate problem is constructed with branched multipoint approximate functions to replace the primal problem. To solve this approximate problem, genetic algorithm is firstly used to optimize discrete variables, and meanwhile, a blending design scheme is proposed to generate a blended structure. Starting from the thinnest region, this scheme shares all layers of current thinnest region with its adjacent regions. For non-shared layers in the adjacent regions, local mutation is implemented to add or delete plies to make them efficient designs. The whole process is repeated until the blending rule is satisfied. After that, a second-level approximate problem is built to optimize the continuous variables of ply thicknesses for retained layers. Those procedures are repeated until the optimal solution is obtained. Numerical applications, including a two-patch panel and a corrugated central cylinder in a satellite, are conducted to demonstrate the efficacy of the optimization strategy.

     

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

 In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask fill and a special boolean based function application. Both phases are optimized to reach a more efficient, fast and flexible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve efficiency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated.     

Yule’s Characteristic K Revisited

The measure of lexical repetition constitutes one of the variables used to determine the lexical richness of literary texts, a value further employed in authorship attribution studies. Although most of the constants for lexical richness actually depend on text length, Yule’s characteristic is considered to be highly reliable for being text length independent. It is not the aim of this paper questioning the validity of K to measure the lexical repeat-rate, nor to evaluate its usefulness in authorship studies, but to review the most accurate procedure to calculate its value in the light of the lack of standardization found in the specific literature. At the same time, the peculiar calculation of Yule’s K by TACT is explained. Our study suggests that standardization will certainly help improve the studies where K is employed.     

多目标约束处理方法求解泊位岸桥联合分配问题

为了制定合理高效的泊位岸桥联合分配方案,加快船舶周转,本文针对船舶动态到港的连续泊位建立了以船舶总在港时间最短为目标的泊位岸桥联合分配混合整数非线性模型.通过多目标约束处理策略将复杂约束的违反程度转化为另一个目标,从而将原单目标优化模型转化为双目标优化模型,并用基于快速非支配排序的多目标遗传算法(NSGA-II)对其进行求解.同时,针对问题特点,分别设计了基于调整、惩罚函数、可行解优先和综合约束处理策略的单目标遗传算法对原模型进行求解.通过多组不同规模的标准算例对本文的方法进行测试,验证了基于多目标约束处理策略的方法求解效果相较于单目标约束处理策略的方法更加高效和稳定.     

A mixed-discrete Particle Swarm Optimization algorithm with explicit diversity-preservation

Engineering design problems often involve non-linear criterion functions, including inequality and equality constraints, and a mixture of discrete and continuous design variables. Optimization approaches entail substantial challenges when solving such an all-inclusive design problem. In this paper, a modification of the Particle Swarm Optimization (PSO) algorithm is presented, which can adequately address system constraints while dealing with mixed-discrete variables. Continuous search (particle motion), as in conventional PSO, is implemented as the primary search strategy; subsequently, the discrete variables are updated using a deterministic nearest-feasible-vertex criterion. This approach is expected to alleviate the undesirable difference in the rates of evolution of discrete and continuous variables. The premature stagnation of candidate solutions (particles) due to loss of diversity is known to be one of the primary drawbacks of the basic PSO dynamics. To address this issue in high dimensional design problems, a new adaptive diversity-preservation technique is developed. This technique characterizes the population diversity at each iteration. The estimated diversity measure is then used to apply (i) a dynamic repulsion away from the best global solution in the case of continuous variables, and (ii) a stochastic update of the discrete variables. For performance validation, the Mixed-Discrete PSO algorithm is applied to a wide variety of standard test problems: (i) a set of 9 unconstrained problems, and (ii) a comprehensive set of 98 Mixed-Integer Nonlinear Programming (MINLP) problems. We also explore the applicability of this algorithm to a large scale engineering design problem—-wind farm layout optimization.     

Specifications, Design and Evaluation of an Advanced Human-Adapted Supervisory System

Supervision of highly automated processes is an interdisciplinary research area. Knowledge in the fields of automation, process knowledge, machine engineering, ‘work post’ ergonomics, cognitive ergonomics, working psychology, sociology and so on is necessary to design efficient supervisory systems. This is because supervision is an activity in which man, despite the increasing automation of recent years, is still present. Our research concerns monitoring tasks and diagnosis tasks in continuous processes. In this paper we propose specifications for an advanced human-adapted supervisory system (AHASS) integrating representation characteristics of the production system, such as functional, structural and behavioural aspects based on cognitive engineering models, with the use of advanced algorithms of detection and location. The main idea is to design a supervisory system well balanced between human and technical aspects. Indeed, man–machine system-centred approaches can deal to another extreme like purely technical approaches. These specifications have been used to design an AHASS for a nuclear fuel reprocessing system that has been evaluated through experiments with experienced operators. The results show that the approach is interesting because the boarder between support and assistantship is never crossed.