Selection of appropriate multilayered plate theories by using a genetic like algorithm

This paper evaluates a number of classical and refined two-dimensional theories for the analysis of metallic and composite layered plates. Thin-plate, shear deformation and higher order plate theories are compared for various plate problems related to different mechanical and geometrical boundary conditions (BCs), as well as geometries and staking sequence lay-out. The theories are implemented by referring to a Unified Formulation (UF) proposed by the first author. The UF allows displacement fields with any order N in the thickness plate direction to be introduced and any variables in the N-order displacement field to be discarded. The finite element method is applied to include anisotropy and complex BCs. The accuracy of given theories for each fixed problem is established in terms of displacement and stress fields. The best plate theories, that is the most accurate plate theories with few computational efforts, is then determined by exploring various possibilities and by selecting appropriate unknown variables upon application of genetic algorithms. A best plate curve is established which shows the best plate theories (number of terms and their meanings) in terms of accuracy. It is concluded that a best plate theory changes with changing geometry, lay-out and BCs. The genetic algorithm used allows the least expensive computational model of each given problem to be detected.     

An optimization technique using the characteristics of genetic algorithm

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.     

Scheduling trucks in container terminals using a genetic algorithm

Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.     

Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.     

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.     

Camera calibration using a genetic algorithm

An autonomous robot will have to detect moving obstacles online before it can plan its collision-free path, while navigating in a dynamic environment. The robot collects information about the environment with the help of a camera and determines the inputs for its motion planner through image analysis. The present article deals with issues related to camera calibration and online image processing. The problem of camera calibration is treated as an optimization problem and solved using a genetic algorithm so as to achieve minimum distorted image plane error. The calibrated vision system is then utilized for the detection and identification of the objects by analysing the images collected at regular intervals. For image processing, five different operations, such as median filtering, thresholding, perimeter estimation, labelling and size filtering, have been carried out. To show the effectiveness of the developed camera-based vision system, inputs of the motion planner of a navigating robot are calculated for two different cases. It is observed that online detection of the shapes and configurations of the obstacles is possible by using the vision system developed.     

Design optimization of a two-dimensional hydrofoil by applying a genetic algorithm

In an effort to optimize river-flow training structures, a study is undertaken to explore the utility of genetic algorithms. The study includes the development of a numerical procedure for optimization of a two-dimensional hydrofoil; the optimization of shape is performed using a genetic algorithm. A formula utilizing two Bezier splines for the construction of the foil shape is introduced. The search for the optimal shape is translated to one of determining the coordinates of the vertex points of the two Bezier splines which control the upper and lower surfaces of the foil. A genetic algorithm is employed as an optimization tool. The methodology developed is applied to the determination of hydrofoil shapes under three different objective functions. The shapes produced by the genetic algorithm all yield good performance with high lift and low drag, which are the desirable characteristics for river-flow training structures.     

Constrained optimization of test intervals using a steady-state genetic algorithm

There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Intervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effective and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burden is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in the constrained optimization of TIs based on risk and cost models at system level are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constraints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a great benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constrained optimization problems as it is concluded in this paper.     

Price negotiation for capacity sharing in a two-factory environment using genetic algorithm

Uncertain and lumpy demand forces capacity planners to maximize the profit of individual factory by simultaneously taking advantage of outsourcing to and/or being outsourced from its supply chain and even competitors. This study develops a resource-planning model of a large manufacturer with two profit-centered factories. The proposed model enables a collaborative integration for resource and demand sharing which is highly attractive to the high-tech industries against the challenges of short product life cycle, intensive capital investment and decreasing marginal profit. Each of the individual factories applies an economic resource-planning model and a genetic algorithm to improve its objective while purchasing extra capacity requirement from its peer factory or selling extra capacity of resources to the others through a negotiation algorithm. This study makes a contribution in successfully building a mutual negotiation model for a set of customer tasks to be realized by the negotiating parties, each with private information regarding company objectives, cost and price. Experimental results reveal that near-optimal solutions for both of the isolated (a single factory) and negotiation-based (between two factories) environments are obtained.     

A new hybrid real-coded genetic algorithm and its application to parameters identification of soils

Inverse analysis using an optimization method based on a genetic algorithm (GA) is a useful tool for obtaining soil parameters in geotechnical fields. However, the performance of the optimization in identifying soil parameters mainly depends on the search ability of the GA used. This study aims to develop a new efficient hybrid real-coded genetic algorithm (RCGA) being applied to identify parameters of soils. In this new RCGA, a new hybrid strategy is proposed by adopting two crossovers with outstanding ability, namely the Simulated Binary Crossover and the simplex crossover. In order to increase the convergence speed, a chaotic local search technique is used conditionally. The performance of the proposed RCGA is first validated by optimizing mathematical benchmark functions. The results demonstrate that the RCGA has an outstanding search ability and faster convergence speed compared to other hybrid RCGAs. The proposed new hybrid RCGA is then further evaluated by identifying soil parameters based on both laboratory tests and field tests, for different soil models. All the comparisons demonstrate that the proposed RCGA has an excellent performance of inverse analysis in identifying soil parameters, and is thus recommended for use based on all the evaluations carried out in this paper.     

Cutting path optimization in CNC cutting processes using a two-step genetic algorithm

This paper deals with the problem of generating 2D cutting paths for a stock plate nested with a set of regular and/or irregular parts. The objective of the problem is to minimize the total non-productive traveling distance of a cutter starting from a known depot, then cutting all the given parts, and returning back to the depot. A cutting path consists of the depot and piercing points, each of which is to be specified for cutting a part. The cutting path optimization problem is shown to be formulated as a generalized version of the standard traveling salesman problem. To solve the problem, a two-step genetic algorithm combining global search for piercing point optimization and local search for part sequencing is proposed. Traditional genetic operators developed for continuous optimization problems are modified to effectively deal with the continuous nature of piercing-point positions. A series of computational results are provided to illustrate the validity of the proposed algorithm.     

Optimisation of fibre steering in composite laminates using a genetic algorithm

An optimization procedure using a genetic algorithm has been applied to define the optimum orientation of fibres in a uni-directional laminate in which the fibres were allowed to vary continuously across the domain. The domain was divided into two-dimensional finite elements and anisotropic properties corresponding to a carbon fibre laminate with all layers aligned in the zero element axis direction were applied to the laminate. The orientation of the material axis on each element was then prescribed as an independent variable for the genetic algorithm.     

Pareto optimization using the struggle genetic crowding algorithm

Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal search spaces. Other published multi-objective genetic algorithms are less robust in locating both global and local Pareto frontiers in a single optimization. For example, in a multi-modal test problem a previously published non-dominated sorting GA (NSGA) located the global Pareto frontier in 41% of the optimizations, while the proposed method located both global and local frontiers in all test runs. Additionally, the algorithm requires little problem specific tuning of parameters.     

Structural damage identification based on modified Artificial Bee Colony algorithm using modal data

A method for structural damage identification based on a modified Artificial Bee Colony algorithm is presented. A new formula is introduced to the onlooker bee phase to improve the convergence rate and the Tournament Selection Strategy is adopted instead of roulette to enhance global search ability of the algorithm. Test functions are introduced as benchmarks to verify the proposed algorithm. And then two numerical examples, including a supported beam and a plate, are conducted to investigate the efficiency and correctness of the proposed method. Final estimated results show that the present technique can produce more accurate damage identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements.     

基于遗传算法的发动机冷却模块振动优化

发动机冷却模块(CRFM)是汽车怠速时的主要振动噪声源之一,本文以某轿车V6发动机的冷却模块为研究对象,针对其怠速开空调时冷却模块及方向盘振动问题,结合数值仿真与试验对比,分析了冷却模块振动的影响因素。冷却模块振动模型的搭建,借助了Matlab,运用能量解耦理论、Newmark-Beta算法,考虑了车架及风扇振动,并采用混合遗传算法对减振连接块的性能进行了优化,结果得到了仿真与试验的确认。     

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.     

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.     

Crack identification by ‘arrival time’ using XFEM and a genetic algorithm

A computational framework is developed in which cracks in two‐dimensional structures are identified, in conjunction with non‐destructive testing of specimens. As opposed to a previous study by the authors, which was based on time‐harmonic excitation with a single frequency, here the transient response of the structure to a short‐duration signal is measured along part of the external boundary. Crack detection is performed using the solution of an inverse time‐dependent problem. It is shown that the arrival time of the input signal to the points of measurement is a good criterion for crack identification in the time domain. The inverse problem of identification is solved using a genetic algorithm, while each forward problem is solved by the time‐dependent extended finite element method (XFEM). The XFEM scheme is efficient in that it allows the use of a single regular mesh for a large number of forward time response problems with different crack geometries. Numerical examples involving a crack in a flat membrane are presented. Identification based on ‘arrival time’ is shown to perform better than that based on time‐harmonic response. Copyright © 2008 John Wiley & Sons, Ltd.     

Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.     

Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.