Evolutionary structural optimization for dynamic problems

This paper presents a simple method for structural optimization with frequency constraints. The structure is modelled by a fine mesh of finite elements. At the end of each eigenvalue analysis, part of the material is removed from the structure so that the frequencies of the resulting structure will be shifted towards a desired direction. A sensitivity number indicating the optimum locations for such material elimination is derived. This sensitivity number can be easily calculated for each element using the information of the eigenvalue solution. The significance of such an evolutionary structural optimization (ESO) method lies in its simplicity in achieving shape and topology optimization for both static and dynamic problems. In this paper, the ESO method is applied to a wide range of frequency optimization problems, which include maximizing or minimizing a chosen frequency of a structure, keeping a chosen frequency constant, maximizing the gap of arbitrarily given two frequencies, as well as considerations of multiple frequency constraints. The proposed ESO method is verified through several examples whose solutions may be obtained by other methods.     

Evolutionary robustness analysis for multi-objective optimization: benchmark problems

This paper presents a new approach to robustness analysis in multi-objective optimization problems aimed at obtaining the most robust Pareto front solutions and distributing the solutions along the most robust regions of the optimal Pareto set. A new set of test problems accounting for the different types of robustness cases is presented in this study. Non-dominated solutions are classified according to their degree of robustness and are distributed along the Pareto front according to specific algorithm parameter values. Verification of the proposed method is carried out using the developed test problems and artificial and real world benchmark test problems present in the literature.     

混合性能指标优化问题的进化优化方法及应用

针对混合性能指标优化问题的普遍性及其处理过程中的特点,提出一种混合性能指标优化问题的进化优化方法,首先给出混合性能指标优化问题的定义;然后．确定不同类型和标度的性能指标的转换策略、混合性能指标的个体适应度的赋值方法、以及混合性能指标优化问题的进化优化流程;最后．通过服装设计这一典型的混合性能指标优化问题的仿真验证了算法的有效性．     

Automated data capture techniques: A prerequisite for effective integrated manufacturing systems

As business environments become more and more information-intensive, an organization's approach to collecting, processing and disseminating information becomes a significant determinant of its productivity, delivery performance, quality, profitability, and overall competitiveness. Integrated Manufacturing Systems (IMS) of the 1990s and beyond will require a totally integrative management of the manufacturing enterprise. This focus on the integration of the entire enterprise rather than a narrow focus in just the manufacturing function places more stringent demands on data integrity in order to effectively coordinate the activities required to carry out the functions aimed at attaining the organizational mission. An overview is presented on the current body of knowledge on Automated Data Capture (ADC) technologies, including: bar coding, radio frequency identification, radio frequency communication, optical character recognition, voice recognition systems, machine vision, magnetic stripes, Electronic Data Interchange (EDI), and systems design and integration considerations. These technologies, collectively referred to as keyless data entry techniques, automated data capture, techniques, or Automatic Identification (Auto ID) technologies are critical to the success of automated manufacturing systems, distribution, inventory control and other computer facilitated activities related to enterprise integration.     

An integrated form-feature-based design system for manufacturing

Much of the knowledge that is applied in or communicated between design and manufacturing activities is primarily shape based or shape indexed. Previous attempts to acquire and organize shape knowledge have been mostly concentrated on feature recognition from solid models, group technology (GT) coding schemes, and feature-based modeling. This paper presents the development of an efficient form-feature-based modeling system, and addresses the important issue of utilizing feature information for manufacturing, which has not been extensively discussed by previous work. In this paper we first present a Euler operator-based approach for efficient and effective form-feature encoding and manipulation in a feature-based design environment. Subsequently, a hybrid representation scheme called enhanced CSG tree of feature (ECTOF), which integrates feature model with solid model in a tree structure, is discussed. A feature interference resolution methodology to maintain the correct and consistent feature information in an ECTOF is also deliberated. Finally, we present a machinability-checking module, which employs global accessibility criteria to analyze a feature's machinability on a three-axis machining center. By developing feature interference resolving and machinability testing techniques and integrating with an efficient feature-based design system, this research makes the development of an integrated feature-based design and manufacturing system possible.     

Evolutionary techniques versus swarm intelligences: application in reservoir release optimization

In this paper, a nonlinear reservoir release optimization problem has been solved by using four optimization tools with various combinations of input parameters that are generally used in this research field. A comparison has been made between evolutionary methods [genetic algorithm (GA)] and swarm intelligences [particle swarm optimization (PSO) and artificial bee colony (ABC) optimization] in searching the optimum reservoir release policy. From the historical recorded data, the monthly inflow was categorized into three states: high, medium and low. As a guideline for the decision maker, an optimum release curve was generated for each month showing the release options with a variety of different storage conditions. GA (real and binary), ABC optimization and PSO algorithm have been used as optimization tools with the same formulation and objective function for all the methods. For verification of the models, a simulation is done by using 264 monthly historical inflow data. Different indices such as reliability, vulnerability and resiliency were calculated in order to check the performance and risk analysis purposes. The results show that the most recently developed ABC optimization technique provides the best results in meeting demands, avoiding wastage of water and in handling critical period of low flows.     

Evolutionary algorithm characterization in real parameter optimization problems

This paper deals with the constant problem of establishing a usable and reliable evolutionary algorithm (EA) characterization procedure so that final users like engineers, mathematicians or physicists can have more specific information to choose the most suitable EA for a given problem. The practical goal behind this work is to provide insights into relevant features of fitness landscapes and their relationship to the performance of different algorithms. This should help users to minimize the typical initial stage in which they apply a well-known EA, or a modified version of it, to the functions they want to optimize without really taking into account its suitability to the particular features of the problem. This trial and error procedure is usually due to a lack of objective and detailed characterizations of the algorithms in the literature in terms of the types of functions or landscape characteristics they are well suited to handle and, more importantly, the types for which they are not appropriate. Specifically, the influence of separability and modality of the fitness landscapes on the behaviour of EAs is analysed in depth to conclude that the typical binary classification of the target functions into separable/non-separable and unimodal/multimodal is too general, and characterizing the EAs’ response in these terms is misleading. Consequently, more detailed features of the fitness landscape in terms of separability and modality are proposed here and their relevance in the EAs’ behaviour is shown through experimentation using standardized benchmark functions that are described using those features. Three different EAs, the genetic algorithm, the Covariance Matrix Adaptation Evolution Strategy and Differential Evolution, are evaluated over these benchmarks and their behaviour is explained in terms of the proposed features.     

Advanced material handling system for computer integrated manufacturing

A cordless linear synchronous motor (LSM) material handling system for CIM has been developed. The system incorporates a courier that moves without tether restrictions. This provides for an automated material handling system with improved travel and flexibility. The material handling system runs on an integrated reverse air bearing system which eliminates friction and backlash. The proposed system forms part of a manufacturing assembly, interacting with overhead manipulators and part feeders to form a comprehensive production system. The design and implementation of the cordless linear motor material handling system (MHS) is outlined and described in the paper.     

Evolutionary multi-objective optimization in robot soccer system for education

As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a multiobjective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Paretooptimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.     

Evolutionary optimization of multi-parametric kernel \epsilon-SVMr for forecasting problems

In this paper, we propose a novel multi-parametric kernel Support Vector Regression algorithm (SVMr) optimized with an evolutionary technique, specially well suited for forecasting problems. The multi-parametric SVMr model and the evolutionary algorithm proposed are both described in detail in the paper. In addition, several new bounds for the multi-parametric kernel considered are obtained, in such a way that the SVMr hyper-parameters’ search space is reduced. We present experimental evidences of the good performance of the evolutionary algorithm for optimizing the multi-parametric kernel, when compared to a standard SVMr with a Grid Search approach. Specifically, results in different real regression problems from public repositories are obtained, and also a real application focused on the short-term temperature prediction at Barcelona’s airport. The results obtained have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.     

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm.     

一类带约束动态多目标优化问题的进化算法

动态多目标约束优化问题是一类NP-Hard问题,定义了动态环境下进化种群中个体的序值和个体的约束度,结合这两个定义给出了一种选择算子.在一种环境变化判断算子下给出了求解环境变量取值于正整数集Z+的一类带约束动态多目标优化问题的进化算法.通过几个典型的Benchmark函数对算法的性能进行了测试,其结果表明新算法能够较好地求出带约束动态多目标优化问题在不同环境下质量较好、分布较均匀的Pareto最优解集.     

Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation

The canonical firefly algorithm is basically developed for continuous optimization problems. However, lots of practical problems are formulated as discrete optimization problems. The main purpose of this paper is to present the discrete firefly algorithm (DFA) to solve discrete optimization problems. In the DFA, we define a firefly's position in terms of changes of probabilities that will be in one state or the other. Then by using this metaheuristic algorithm, the manufacturing cell formation problem is solved. To illustrate the behavior of the proposed model and verify the performance of the algorithm, we introduce a number of numerical examples to illustrate the use of the foregoing algorithm. The performance evaluation shows the effectiveness of the DFA. The proposed metaheuristic algorithm should thus be useful to both researchers and practitioners.     

A foundation for a knowledge-base computer integrated manufacturing system

The true potential of a CIM system cannot as yet be fully realised. A further requirement is for a device which can extract manufacturing information from the outset. The main aim of the research described in this paper is to develop a knowledge-base system for extracting such information and to promote a coming together of design and manufacture. The major goal must be to enable industry to manufacture without manual intervention. A prototype knowledge-base system called HOLDEX described in this paper is an implementation that relies on combining the advantages of existing technology such as AI, CAD representation schemes, advanced manufacturing systems, man-machine interface and most importantly the encapsulated knowledge of a manufacturing engineer. The aim of the prototype system is to encourage a workpiece designer towards manufacturing standards and to assist him in the use of a general purpose CAD package. One benefit to be obtained from providing such assistance is to facilitate the ease of extracting manufacturing information. The scope of the extracted information includes the detailed specification of a set of cutting tools, the recognition of a list of required activities prior to a machining operation, the recommendation of the appropriate machine tool and the automated design of fixtures.     

铅锌烧结过程智能集成优化控制技术

针对复杂的铅锌烧结过程。提出了基于神经网络和模糊专家规则模型的自学习模糊专家控制方法进行状态参数优化，基于神经网络模型的模糊C均值聚类搜索混沌遗传算法进行综合指标参数优化．智能集成优化控制技术具有高效性和实用性，有效地抑制了工况波动，提高了烧结矿产量和质量，取得了较好的工业控制效果．     

Evolutionary structural optimization for stress minimization problems by discrete thickness design

Stress minimization is a major aspect of structural optimization in a wide range of engineering designs. This paper presents a new evolutionary criterion for the problems of variable thickness design whilst minimizing the maximum stress in a structure. On the basis of finite element analysis, a stress sensitivity number is derived to estimate the stress change in an element due to varying the thickness of other elements. Following the evolutionary optimization procedure, an optimal design with a minimum maximum stress is achieved by gradually removing material from those elements, which have the lowest stress sensitivity number or adding material onto those elements, which have the highest stress sensitivity number. The numerical examples presented in this paper demonstrate the capacity of the proposed method for solving stress minimization problems. The results based on the stress criterion are compared with traditional ones based on a stiffness criterion, and an optimization scheme based on the combination of both the stress minimization and the stiffness maximization criteria is presented.     

A knowledge-based system for information management in an automated and integrated manufacturing system

In recent years there has been considerable interest in flexible automation of manufacturing processes in an effort to improve the productivity of manufacturing industry. Central to an integrated, flexible and intelligent manufacturing system is the information management function. The sharing of data is the basis of flexible and intelligent decision-making, the means of integrating the system components, the mode of synchronizing their processes and the method of maintaining the consistency of their states.

It appears that the data management and manipulation function in manufacturing systems is increasingly becoming a bottleneck for further performance improvement of manufacturing systems. New information management systems need to be developed that meet the needs of modern manufacturing systems in terms of efficiency, flexibility, reliability and intelligence. A new model for an integrated manufacturing information system is presented. A double structure of two parallel but independent system is proposed for manufacturing control and manufacturing information management. The objective is to improve service efficiency. representational adequacy, consistency of information and reliability in the information system.     

An integrated expert system/operations research approach for the optimization of waste incinerator siting problems

This research aims at developing an integrated decision support system for the optimization of waste incinerator siting problems. In this integrated approach, both expert system and operations research techniques are used to model the siting problems of waste incinerator. Furthermore, an expert decision support system (EDSS) is implemented for the above problem and thus providing the decision makers a useful tool for decision-making. This EDSS is based on multi-criteria decision analysis in finding the best incinerator site by minimizing costs and environmental impacts. The proposed approach identifies a hierarchy of objectives for the siting problem. First of all, several potential sites need to be screened as a set of feasible alternative sites. Second, those alternative feasible sites will be further evaluated via the multi-criteria decision making methods. For the evaluation process, we solve a 0/1 combinatorial optimization problem at the upper level and proceed the multi-attribute utility function at the lower level to get the optimal solutions. An empirical application of a real world waste incinerator site selection existing in Taichung City, Taiwan is followed in the end. Computational results both of the cost minimization and of the whole systems are also provided.     

Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems

Evolutionary algorithms (EAs) are often employed to multiobjective optimization, because they process an entire population of solutions which can be used as an approximation of the Pareto front of the tackled problem. It is a common practice to couple local search with evolutionary algorithms, especially in the context of combinatorial optimization. In this paper a new local search method is proposed that utilizes the knowledge concerning promising search directions. The proposed method can be used as a general framework and combined with many methods of iterating over a neighbourhood of an initial solution as well as various decomposition approaches. In the experiments the proposed local search method was used with an EA and tested on 2-, 3- and 4-objective versions of two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). For comparison two well-known local search methods, one based on Pareto dominance and the other based on decomposition, were used with the same EA. The results show that the EA coupled with the directional local search yields better results than the same EA coupled with any of the two reference methods on both the TSP and QAP problems.