Multiobjective firefly algorithm for continuous optimization

Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.     

Interdigitation for effective design space exploration using iSIGHT

Optimization studies for nonlinear constrained problems (i.e. most complex engineering design problems) have repeatedly shown that (i) no single optimization technique performs best for all design problems, and (ii) in most cases, a mix of techniques perform better than a single technique for a given design problem. iSIGHT^TM is a generic software framework for integration, automation, and optimization of design processes that has been developed on the foundation of interdigitation: the strategy of combining multiple optimization algorithms to exploit their desirable aspects for solving complex problems. With the recent paradigm shift from traditional optimization to design space exploration for evaluating “what-if” scenarios and trade-off studies, iSIGHT has grown from an optimization software system to a complete design exploration environment, providing a suite of design exploration tools including a collection of optimization techniques, design of experiments techniques, approximation methods, and probabilistic quality engineering methods. Likewise, the interdigitation design methodology embodied in iSIGHT has grown to support the interdigitation of all design exploration tools for effective design space exploration. In this paper we present an overview of iSIGHT, past and present, of the interdigitation design methodology and its implementation for multiple design exploration tools, and of an industrial case study for which elements of this methodology have been applied. Received December 30, 2000     

Mathematical programming models and algorithms for engineering design optimization

Mathematical programming provides general tools for engineering design optimization. We present numerical models for simultaneous analysis and design optimization (SAND) and multidisciplinary design optimization (MDO) represented by mathematical programs. These models are solved with numerical techniques based on the feasible arc interior point algorithm (FAIPA) for nonlinear constrained optimization. Even if MDO is a very large optimization problem, our approach reduces considerably the computer effort. Several tools for very large problems are also presented. The present approach is very strong and efficient for real industrial applications and can easily interact with existing simulation engineering codes.     

基于安腾微处理器的程序性能优化与分析

高性能计算越来越广泛地应用到科学和工程的各个领域,但实际应用程序获得的性能并未随着机器峰值性能的提高而同比例提高,应用程序只能发挥峰值性能的5%～10%左右,而且两者的差距在扩大,程序性能优化作为解决该问题的方法之一得到了学术界的广泛关注。本文基于安腾微处理器,总结了程序优化的通用方法,给出了程序优化与分析的一般步骤。根据优化与分析步骤,首先对四个程序进行了详细的性能分析,找到性能瓶颈和重点子程序;然后分别根据四个程序的特点,采用基于Cache和指令流水线的优化技术,对程序进行了性能优化;最后给出了性能优化测试结果,分别得到8%～33%的性能提高,取得了良好的优化效果。     

Evolutionary Algorithms for Minimax Problems in Robust Design

Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem.     

An optimality criteria based method for discrete design optimization taking into account buildability constraints

This paper presents a heuristic design optimization method specifically developed for practicing structural engineers. Practical design optimization problems are often governed by buildability constraints. The majority of optimization methods that have recently been proposed for design optimization under buildability constraints are based on evolutionary computing. While these methods are generally easy to implement, they require a large number of function evaluations (finite element analyses), and they involve algorithmic parameters that require careful tuning. As a consequence, both the computation time and the engineering time are high. The discrete design optimization algorithm presented in this paper is based on the optimality criteria method for continuous optimization. It is faster than an evolutionary algorithm and it is free of tuning parameters. The algorithm is successfully applied to two classical benchmark problems (the design of a ten-bar truss and an eight-story frame) and to a practical truss design optimization problem.     

Multiparametric optimization for multidisciplinary engineering design

The area of Multiparametric Optimization (MPO) solves problems that contain unknown problem data represented by parameters. The solutions map parameter values to optimal design and objective function values. In this paper, for the first time, MPO techniques are applied to improve and advance Multidisciplinary Design Optimization (MDO) to solve engineering problems with parameters. A multiparametric subgradient algorithm is proposed and applied to two MDO methods: Analytical Target Cascading (ATC) and Network Target Coordination (NTC). Numerical results on test problems show the proposed parametric ATC and NTC methods effectively solve parametric MDO problems and provide useful insights to designers. In addition, a novel Two-Stage ATC method is proposed to solve nonparametric MDO problems. In this new approach elements of the subproblems are treated as parameters and optimal design functions are constructed for each one. When the ATC loop is engaged, steps involving the lengthy optimization of subproblems are replaced with simple function evaluations.     

Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function

Many engineering, science, information technology and management optimization problems can be considered as non-linear programming real-world problems where all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non-linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers, which was represented by logistic membership functions using the hybrid evolutionary optimization approach. To explore the applicability of the present study, a numerical example is considered to determine the production planning for the decision variables and profit of the company.     

用演化算法求解抛物型方程扩散系数的识别问题

基于演化算法给出了一类求解参数识别反问题的一般方法,该方法表明只要找到好的、求解相应的正问题的数值方法,演化算法就可以用于求解此类反问题。设计有效的求解反问题的演化算法的关键是寻找一种适合反问题的解空间的编码表示形式、适当的适应值函数形式以及有效的计算正问题的数值方法。该文结合算法、传统的求解反问题的工方法和正则化技术,设计了一类求解参数识别反问题的方法。为验证此类方法,将其用于求解一维扩散方程的     

Solving equations by hybrid evolutionary computation techniques

Evolutionary computation techniques have mostly been used to solve various optimization and learning problems. This paper describes a novel application of evolutionary computation techniques to equation solving. Several combinations of evolutionary computation techniques and classical numerical methods are proposed to solve linear and partial differential equations. The hybrid algorithms have been compared with the well-known classical numerical methods. The experimental results show that the proposed hybrid algorithms outperform the classical numerical methods significantly in terms of effectiveness and efficiency     

基于MSC Nastran的离散变量优化算法的实现

实际工程中存在大量的离散变量优化问题,基于MSC Nastran优化框架实现新的离散变量算法,有利于新算法本身的推广应用和解决大规模的实际复杂工程问题.通过修改MSC Nastran输入文件的方法实现离散变量的优化算法——GSFP算法.GSFP是基于广义形函数的离散变量优化算法,它将离散变量优化问题转化成连续变量优化问题,通过惩罚等措施使得最优设计结果最终收敛到离散解,该方法能够解决大规模的实际离散变量优化问题.最后以桁架截面选型优化为应用背景,给出GSFP算法实现的基本原理和方法.     

COOPT — a software package for optimal control of large-scale differential–algebraic equation systems

This paper describes the functionality and implementation of COOPT. This software package implements a direct method with modified multiple shooting type techniques for solving optimal control problems of large-scale differential–algebraic equation (DAE) systems. The basic approach in COOPT is to divide the original time interval into multiple shooting intervals, with the DAEs solved numerically on the subintervals at each optimization iteration. Continuity constraints are imposed across the subintervals. The resulting optimization problem is solved by sparse sequential quadratic programming (SQP) methods. Partial derivative matrices needed for the optimization are generated by DAE sensitivity software. The sensitivity equations to be solved are generated via automatic differentiation.COOPT has been successfully used in solving optimal control problems arising from a wide variety of applications, such as chemical vapor deposition of superconducting thin films, spacecraft trajectory design and contingency/recovery problems, and computation of cell traction forces in tissue engineering.     

Shape sensitivity analysis using the indirect boundary element method

The paper describes a novel formulation for the computation of the design sensitivities required for shape optimization problems using the indirect boundary element method. As a first stage, the system of equations that evaluate the fictitious traction sensitivities is differentiated with respect to shape design variables. The stress or displacement sensitivities are then evaluated by direct substitution of the fictitious traction sensitivities into the differentiated stress or displacement kernels. Two other finite difference-based techniques for the evaluation of the stress sensitivities, using the indirect boundary element method are also presented. The advantages and the drawbacks of each approach are discussed. These methods have been shown to be effective, accurate and can be incorporated in an existing BE code with much less programming effort than other BE-based techniques. The efficiency of the three methods is illustrated by optimizing the shape of a 90° V-notch. In all cases, convergence is achieved within three to four iterations.Various approximate techniques are suggested to minimize the computation cost of the optimization problem. These techniques are based on the fundamental features of the stress field, the differentiated kernels and the system of matrices of the optimization problem. Investigations have shown that employing these techniques yields more than a 50% reduction in computer time with insignificant loss of accuracy.     

约束进化算法及其应用研究综述

约束优化问题广泛存在于科学研究和工程实践中,其对应的约束优化进化算法也成为了进化领域的重要研究方向。约束优化进化算法的本质问题是如何有效地利用不可行解和可行解的信息,平衡目标函数和约束条件,使得算法更加高效。首先对约束优化问题进行定义;然后详细分析了目前主流的约束进化算法,同时,基于不同的约束处理机制,将这些机制分为约束和目标分离法、惩罚函数法、多目标优化法、混合法和其他算法,并对这些方法进行了详细的分析和总结;接着指出约束进化算法亟待解决的问题,并明确指出未来需要进一步研究的方向;最后对约束进化算法在工程优化、电子和通信工程、机械设计、环境资源配置、科研领域和管理分配等方面的应用进行了介绍。     

A DPS-based system modelling method for 3D-structures simulation in manufacturing processes

In the field of engineering, guidelines and computerisation are important to facilitate the resolution of complex problems. Engineers apply computer techniques of distributed problem solving (DPS) to design and simulation tasks, such as in industrial manufacture (e.g., simulation of aircraft, modelling of structures and mass customization). An important application of these and other computer-aided techniques is the design and manufacture of industrial greenhouses in south-eastern Spain. The importance of greenhouses in this region led to the establishment of European Standard UNE-EN 13031-1 for the design and construction of structures for commercial production. Computer-assisted techniques are helping to put this European Manufacturing Standard into effect. This article discusses how DPS techniques help to solve problems in industrial manufacturing processes, and presents the case of modelling and simulation of 3D-structures used in greenhouse construction.     

Experience with approximate reliability-based optimization methods II: an exhaust system problem

Traditional reliability-based design optimization (RBDO) requires a double-loop iteration process. The inner optimization loop is to find the reliability and the outer is the regular optimization loop to optimize the RBDO problem with reliability objectives or constraints. It is known that the computation can be prohibitive when the associated function evaluation is expensive. This situation is even worse when a large number of reliability constraints are present. As a result, many approximate RBDO methods, which convert the double loop to a single loop, have been developed. In this research, an engineering problem with a large number of constraints (144) is designed to test RBDO methods based on the first-order reliability method (FORM), including single- and double-loop methods. In addition to the number of constraints, this problem possesses many local minimums. Some original authors of the RBDO methods are also asked to solve the same problem. The results and the efficiencies for different methods are published and discussed.     

Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey

Integrated manufacturing system (IMS) is a novel manufacturing environment which has been developed for the next generation of manufacturing and processing technologies. It consists of engineering design, process planning, manufacturing, quality management, and storage and retrieval functions. Improving the decision quality in those fields give rise to complex combinatorial optimization problems, unfortunately, most of them fall into the class of NP-hard problems. Find a satisfactory solution in an acceptable time play important roles. Evolutionary techniques (ET) have turned out to be potent methods to solve such kind of optimization problems. How to adapt evolutionary technique to the IMS is very challenging but frustrating. Many efforts have been made in order to give an efficient implementation of ET to optimize the specific problems in IMS.In this paper, we address four crucial issues in IMS, including design, planning, manufacturing, and distribution. Furthermore, some hot topics in these issues are selected to demonstrate the efficiency of ET’s application, such as layout design (LD) problem, flexible job-shop scheduling problem (fJSP), multistage process planning (MPP) problem, and advanced planning and scheduling (APS) problem. First, we formulate a generalized mathematic models for all those problems; several evolutionary algorithms which adapt to the problems have been proposed; some test instances based on the practical problems demonstrate the effectiveness and efficiency of our proposed approach.     

On user-centric memetic algorithms

Memetic algorithms (MAs) constitute a metaheuristic optimization paradigm [usually based on the synergistic combination of an evolutionary algorithm (EA) and trajectory-based optimization techniques] that systematically exploits the knowledge about the problem being solved and that has shown its efficacy to solve many combinatorial optimization problems. However, when the search depends heavily on human-expert’s intuition, the task of managing the problem knowledge might be really difficult or even indefinable/impossible; the so-called interactive evolutionary computation (IEC) helps to mitigate this problem by enabling the human user to interact with an EA during the optimization process. Interactive MAs can be constructed as reactive models in which the MA continuously demands the intervention of the human user; this approach has the drawback that provokes fatigue to the user. This paper considers user-centric MAs, a more global perspective of interactive MAs since it hints possibilities for the system to be proactive rather than merely interactive, i.e., to anticipate some of the user behavior and/or exhibit some degree of creativity, and provides some guidelines for the design of two different models for user-centric MAs, namely reactive and proactive search-based schema. An experimental study over two complex NP-hard problems, namely the Traveling Salesman problem and a Gene Ordering Problem, shows that user-centric MAs are in general effective optimization methods although the proactive approach provides additional advantages.