A seismic optimization procedure for reinforced concrete framed buildings based on eigenfrequency optimization

Several seismic optimization methods have been proposed to improve the performance of reinforced concrete framed (RCF) buildings; however, they have not been widely adopted among practising engineers because they require complex nonlinear models and are computationally expensive. This article presents a procedure to improve the seismic performance of RCF buildings based on eigenfrequency optimization, which is effective, simple to implement and efficient. The method is used to optimize a 10-storey regular building, and its effectiveness is demonstrated by nonlinear time history analyses, which show important reductions in storey drifts and lateral displacements compared to a non-optimized building. A second example for an irregular six-storey building demonstrates that the method provides benefits to a wide range of RCF structures and supports the applicability of the proposed method.     

Hybridizing a genetic algorithm with an artificial immune system for global optimization

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.     

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.     

A hybrid topology optimization algorithm for structural design

A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.     

A two-step optimization approach for technology selection

An efficient optimization approach for the technology selection problem is described. Technology selection is a crucial step in the aircraft design process, especially when the performance and econo-mic requirements are not fulfilled for any combination of the configuration design variables. In such a case, the designer must search efficiently within a set of technology options for the optimal combination that achieves the required improvements. When the set of available technologies is large, as is usually the case, a difficult combinatorial optimization problem ensues, resulting in significant time and computational expense. The objective of the new approach is to reduce the computational cost of technology selection by decomposing the process into two smaller sub-problems. The new approach attempts to exploit the structure of the technology compatibility matrix to improve the efficiency of the technology selection process. Results from an application problem are presented and valuable insights and observations are discussed.     

飞机总体设计中的一种多目标鲁棒优化方法

 提出一种基于灵敏度的多目标鲁棒优化方法。针对各维设计变量存在扰动的情况,在原约束多目标优化模型上,附加偏差目标函数,并采用最差估计法对约束条件进行鲁棒可行性调整。采用全局敏度方程方法来计算目标函数和约束函数对设计变量的敏度,进而采用Pareto遗传算法搜索约束多目标优化问题的非劣解集,设计者可以根据不同的设计准则从中选择合适的设计点。将上述方法用于飞机总体参数优化设计,并与采用常规优化方法所得的优化结果进行了分析和比较。     

Multiobjective optimization by genetic algorithms: application to safety systems

When attempting to optimize the design of engineered systems, the analyst is frequently faced with the demand of achieving several targets (e.g. low costs, high revenues, high reliability, low accident risks), some of which may very well be in conflict. At the same time, several requirements (e.g. maximum allowable weight, volume etc.) should also be satisfied. This kind of problem is usually tackled by focusing the optimization on a single objective which may be a weighed combination of some of the targets of the design problem and imposing some constraints to satisfy the other targets and requirements. This approach, however, introduces a strong arbitrariness in the definition of the weights and constraints levels and a criticizable homogenization of physically different targets, usually all translated in monetary terms.The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. For an efficient search through the solution space we use a multiobjective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights.     

A Markov chain analysis of genetic algorithms with individuals having different birth and survival rates

This article studies the convergence characteristics of a genetic algorithm (GA) in which individuals of different age groups in the population possess different survival and birth rates. The inclusion of this feature into the algorithm makes the algorithm mimic the natural evolutionary process more closely than the conventional GA. Although numerical experiments have demonstrated that the proposed algorithm tends to perform better than the conventional GA when used as a function optimizer, the population size of the algorithm is affected by the survival and birth rates of the individuals, which may lead to an unstable search process. Hence, this research develops the condition which governs the birth and survival rates for maintaining a stationary population size during the search process. The Markov chain approach is also used to analyze the convergence characteristics of the algorithm. The proposed algorithm is shown to converge to the global optimal solution if the best candidate solution is maintained over time. The mathematical analysis thus provides a theoretical foundation for the application of the proposed approach as a function optimizer. The performance of the proposed algorithm is tested by solving two benchmark test problems and the results are compared to those obtained by using the conventional GA. Indeed, comparison of the results clearly shows that the proposed approach is superior to the canonical genetic algorithm in terms of the quality of the final solution. The algorithm is described in some detail in the hope of thus stimulating the use of the proposed genetic approach to the solution of important problems in industrial engineering practice.     

Engineering design optimization using species-conserving genetic algorithms

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.     

Techniques based on genetic algorithms for large deflection analysis of beams

A couple of non-convex search strategies, based on the genetic algorithm, are suggested and numerically explored in the context of large-deflection analysis of planar, elastic beams. The first of these strategies is based on the stationarity of the energy functional in the equilibrium state and may therefore be considered weak. The second approach, on the other hand, attempts to directly solve the governing differential equation within an optimisation framework and such a solution may be thought of as strong. Several numerical illustrations and verifications with ‘exact’ solutions, if available, are provided For communication     

基于混合遗传算法的转子系统优化设计

转子系统的临界转速是航空发动机设计过程中的重要参数。在临界转速远离工作转速时,转子系统才能安全可靠的工作。如何设计转子系统的结构使设计后的临界转速达到要求,而且结构改变量尽可能小,是转子动力学最优化设计研究的重点之一。在分析已有研究模型的基础上增加约束条件提出一种更完善的临界转速最优化设计模型,无需考虑设计变量个数和设计临界转速个数的关系,及预先给定的临界转速是否可在设计变量对应的临界转速空间内取到,均能找到满意的设计方案。针对该模型的最优化求解,设计出一种结合遗传算法和复合形方法的混合遗传算法,可以有效的提高搜索到全局最优解的搜索速度。对一转子系统进行临界转速优化设计,验证了该模型可以有效的取得满足设计要求的最优设计方案,适用于工程实际的转子系统临界转速最优化设计过程。     

Design optimization of composites using genetic algorithms and failure mechanism based failure criterion

In this paper, minimum weight design of composite laminates is presented using the failure mechanism based (FMB), maximum stress and Tsai–Wu failure criteria. The objective is to demonstrate the effectiveness of the newly proposed FMB failure criterion (FMBFC) in composite design. The FMBFC considers different failure mechanisms such as fiber breaks, matrix cracks, fiber compressive failure, and matrix crushing which are relevant for different loading conditions. A genetic algorithm is used for the optimization study. The Tsai–Wu failure criterion over predicts the weight of the laminate by up to 86% in the third quadrant of the failure envelope compared to FMB and maximum stress failure criteria, when the laminate is subjected to compressive–compressive loading. It is found that the FMB and maximum stress failure criteria give comparable weight estimates. The FMBFC can be considered for use in the strength design of composite structures.     

A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks

The multistage hybrid flow-shop scheduling problem with multiprocessor tasks has been found in many practical situations. Due to the essential complexity of the problem, many researchers started to apply metaheuristics to solve the problem. In this paper, we address the problem by using particle swarm optimization (PSO), a novel metaheuristic inspired by the flocking behaviour of birds. The proposed PSO algorithm has several features, such as a new encoding scheme, an implementation of the best velocity equation and neighbourhood topology among several different variants, and an effective incorporation of local search. To verify the PSO algorithm, computational experiments are conducted to make a comparison with two existing genetic algorithms (GAs) and an ant colony system (ACS) algorithm based on the same benchmark problems. The results show that the proposed PSO algorithm outperforms all the existing algorithms for the considered problem.     

Balancing multiple criteria in formulation of weighted,single-objective genetic algorithm optimization for CNC machining problems

This paper presents results obtained from the implementation of a genetic algorithm (GA) to a simplified multi-objective machining optimization problem. The major goal is to examine the effect of crucial machining parameters imparted to computer numerical control machining operations when properly balanced conflicting criteria referring to part quality and process productivity are treated as a single optimization objective. Thus the different combinations of weight coefficient values were examined in terms of their significance to the problem's response. Under this concept, a genetic algorithm was applied to optimize the process parameters exist in typical; commercially available CAM systems with significantly low computation cost. The algorithm handles the simplified linear weighted criteria expression as its objective function. It was found that optimization results vary noticeably under the influence of different weighing coefficients. Thus, the obtained optima differentiate, since balancing values strongly affect optimization objective functions.     

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

This article presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple constraint-handling technique. Twenty-four constrained optimization problems commonly adopted in the evolutionary optimization literature, as well as some structural optimization problems are adopted to validate the proposed approach. The results obtained by the proposed approach are compared with respect to those generated by algorithms representative of the state of the art in the area.     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

An adaptive penalty scheme for genetic algorithms in structural optimization

A parameter‐less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems is proposed. Using feedback from the evolutionary process the procedure automatically defines a penalty parameter for each constraint. The user is thus relieved from the burden of having to determine sensitive parameter(s) when dealing with every new constrained optimization problem. The procedure is shown to be effective and robust when applied to test problems from the evolutionary computation literature as well as several optimization problems from the structural engineering literature. Copyright © 2003 John Wiley & Sons, Ltd.     

A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks

Genetic algorithms are currently one of the state-of-the-art meta-heuristic techniques for the optimization of large engineering systems such as the design and rehabilitation of water distribution networks. They are capable of finding near-optimal cost solutions to these problems given certain cost and hydraulic parameters. Recently, multi-objective genetic algorithms have become prevalent in the water industry due to the conflicting nature of these hydraulic and cost objectives. The Pareto-front of solutions can aid decision makers in the water industry as it provides a set of design solutions which can be examined by experienced engineers. However, multi-objective genetic algorithms tend to require a large number of objective function evaluations to arrive at an acceptable Pareto-front. This article investigates a novel hybrid cellular automaton and genetic approach to multi-objective optimization (known as CAMOGA). The proposed method is applied to two large, real-world networks taken from the UK water industry. The results show that the proposed cellular automaton approach can provide a good approximation of the Pareto-front with very few network simulations, and that CAMOGA outperforms the standard multi-objective genetic algorithm in terms of efficiency in discovering similar Pareto-fronts.     

Truss topology,shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution

A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article. Hybrid reproduction and control parameter adaptation strategies are employed to increase the performance of the algorithm. The proposed algorithm, called fully stressed design–grey wolf–adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems. The problems have mixed continuous/discrete design variables that are assigned as simultaneous topology, shape and sizing design variables. FSD-GWADE provides competitive results and gives superior results at a higher success rate than the previous FSD-based algorithm.     

Efficient modelling and optimisation of hybrid multilayered plates subject to ballistic impact

Among the key challenges present in the modelling and optimisation of composite structures against impact is the computational expense involved in setting up accurate simulations of the impact event and then performing the iterations required to optimise the designs. It is of more interest to find good designs given the limitations of the resources and time available rather than the best possible design. In this paper, low cost but sufficiently accurate finite element (FE) models were generated in LS Dyna for several experimentally characterised materials by semi-automating the modelling process and using existing material models. These models were then used by an optimisation algorithm to generate new hybrid offspring, leading to minimum weight and/or cost designs from a selection of isotropic metals, polymers and orthotropic fibre-reinforced laminates that countered a specified impact threat. Experimental validation of the optimal designs thus identified was then successfully carried out using a single stage gas gun. With sufficient computational hardware, the techniques developed in this pilot study can further utilise fine meshes, equations of state and sophisticated material models, so that optimal hybrid systems can be identified from a wide range of materials, designs and threats.