Birefringent filter synthesis by use of a digital filter design algorithm

We present an efficient method for designing birefringent filters comprising a number of birefringent sections with equal length and arbitrary orientation between two polarizers and for producing a specified spectral response in transmission. The method uses a digital filter design algorithm (i.e., the Remez algorithm) to determine an optimal polynomial approximation to obtain a specified finite impulse response, and a layer-peeling algorithm to calculate the filter structure parameters. The design procedure is demonstrated for a 14-section bandpass filter with sidelobes below -40 dB. The influence of errors in length and orientation of the birefringent sections on the filter's spectral response is also discussed.     

Optimisation of the reverse scheduling problem by a modified genetic algorithm

Traditional scheduling methods can only arrange the operations on corresponding machines with appropriate sequences under pre-defined environments. This means that traditional scheduling methods require that all parameters to be determined before scheduling. However, real manufacturing systems often encounter many uncertain events. These will change the status of manufacturing systems. These may cause the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling methods, however, cannot cope with these cases. New scheduling methods are needed. Among these new methods, one method ‘reverse scheduling’ has attracted more and more attentions. This paper focuses on the single-machine reverse scheduling problem and designs a modified genetic algorithm with a local search (MLGA) to solve it. To improve the performance of MLGA, efficient encoding, offspring update mechanism and a local search have been employed and developed. To verify the feasibility and effectiveness of the proposed MLGA, 27 instances have been conducted and results have been compared with existing methods. The results show that the MLGA has achieved satisfactory improvement. This approach also has been applied to solve a real-world scheduling problem from one shipbuilding industry. The results show that the MLGA can bring some benefits.     

Robust design employing a genetic algorithm

The minimization of variability in a key design feature or performance measure, in the presence of variability in the realized values of design parameters, is discussed and an analytic solution for quadratic performance measures is provided. Solutions are based on the determination of optimum nominal (or design point) values for the design parameters, subject to constraints in the form of a given nominal performance at the design point and limits on the nominal values of the design parameters, which preserve the design concept. The more general, numerical, problem solution is addressed and a previously described deterministic procedure which generated multiple local optima is improved by the replacement of a simplex search method with a sophisticated genetic algorithm which, with suitable parameter values and choice of Lagrange multiplier, converges only to the required global minimum within the specified design parameter limits. Further improvements are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

Developments in the use of the genetic algorithm in engineering design

The adaptive search technique known as the genetic algorithm is described and research into its application within the fields of civil, structural and mechanical engineering discussed. Attention is drawn to the potential of the structured genetic algorithm both as a high-level decision support technique during the preliminary stages of the design process and as a powerful tool for the detailed design of complex components. Problems associated with the search for global optimum solutions and design sensitivity are outlined. Increasingly efficient and cost effective hybrid approaches are envisaged which incorporate a range of adaptive search and knowledge-based techniques.     

Multi-objective optimal design of sandwich panels using a genetic algorithm

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.     

Using a modified genetic algorithm to find feasible regions of a desirability function

The multi‐response optimization (MRO) problem in response surface methodology is quite common in applications. Most of the MRO techniques such as the desirability function method by Derringer and Suich are utilized to find one or several optimal solutions. However, in fact, practitioners usually prefer to identify all of the near‐optimal solutions, or all feasible regions, because some feasible regions may be more desirable than others based on practical considerations. In this paper, with benefits from the stochastic property of a genetic algorithm (GA), we present an innovative procedure using a modified GA (MGA), a computational efficient GA with a local directional search incorporated into the GA process, to approximately generate all feasible regions for the desirability function without the limitation of the number of factors in the design space. The procedure is illustrated through a case study. The MGA is also compared with other commonly used methods for determining the set of feasible regions. Using Monte Carlo simulations with two benchmark functions and a case study, it is shown that the MGA can more efficiently determine the set of feasible regions than the GA, grid methods, and the Nelder–Mead simplex algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

Optical design of hybrid-type attenuated phase-shift masks for extreme-ultraviolet lithography by use of a Fabry-Perot interference filter

We have designed what we believe to be new hybrid-type attenuated phase-shift masks for extreme-ultraviolet optical lithography by use of a Fabry-Perot interference filter. The designs for the attenuated phase-shift masks show a smaller step height for less geometric shadow effects than additive- and subtractive-type attenuated phase-shift masks, a contrast higher than 94% for both deep-ultraviolet and extreme-ultraviolet wavelength regimes, and a 180 degrees phase-shift in the extreme-ultraviolet wavelength regime.     

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.     

Optimal quantization method for uneven-phase diffractive optical elements by use of a modified iterative Fourier-transform algorithm

A method based on an iterative Fourier-transform algorithm (IFTA) with phase optimization for phase-only multilevel diffractive optical elements (DOEs) is presented. Phase optimization by minimizing the mean-squared error composed of an amplitude-weighted probability-density function is performed in the IFTA iterations to ensure that the wavefront difference is minimized and thus to improve the DOEs' performance. Using the proposed method, we obtained a small standard deviation of diffraction efficiency of 20 uneven-phase DOEs, showing that DOEs with high diffraction efficiency did not vary significantly with the initial conditions. The simulation results of even- and uneven-phase four-level DOEs designed by use of direct and stepwise quantization IFTA methods are compared.     

Optimal design of integrally gated CNT field-emission devices using a genetic algorithm

A method to optimize the focusing quality of integrally gated CNT field-emission (FE) devices by combining field-emission modeling and a computational intelligence technique, genetic algorithm (GA), is proposed and demonstrated. In this work, the e-beam shape, as a characteristic parameter of electron-optical properties, is calculated by field-emission simulation modeling. Using a design tool that combines GA and physical modeling, a set of structural and electrical parameters for four FE device groups, including double-gate, triple-gate, quadruple-gate and quintuple-gate type, were optimized. The resultant FE devices exhibit satisfactory e-beam focusabilities and the extracted parameters with the best performance for each type of FE device were represented to be fabricated by a VLSI technique. The GA-based automatic design parameter extraction will significantly benefit the design of integrated electron-optical systems for versatile vacuum micro-?and nano-electronic applications.     

Improved genetic algorithm for mixed-discrete-continuous design optimization problems

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.     

Eliminating chromatic aberration in Gauss-type lens design using a novel genetic algorithm

Two different types of Gauss lens design, which effectively eliminate primary chromatic aberration, are presented using an efficient genetic algorithm (GA). The current GA has to deal with too many targets in optical global optimization so that the performance is not much improved. Generally speaking, achromatic aberrations have a great relationship with variable glass sets for all elements. For optics whose design is roughly convergent, glass sets for optics will play a significant role in axial and lateral color aberration. Therefore better results might be derived from the optimal process of eliminating achromatic aberration, which could be carried out by finding feasible glass sets in advance. As an alternative, we propose a new optimization process by using a GA and involving theories of geometrical optics in order to select the best optical glass combination. Two Gauss-type lens designs are employed in this research. First, a telephoto lens design is sensitive to axial aberration because of its long focal length, and second, a wide-angle Gauss design is complicated by lateral color aberration at the extreme corners because Gauss design is well known not to deal well with wide-angle problems. Without numbers of higher chief rays passing the element, it is difficult to correct lateral color aberration altogether for the Gauss design. The results and conclusions show that the attempts to eliminate primary chromatic aberrations were successful.     

Visualization of phase objects by use of a differentiation filter

For the visualization of phase objects by use of a differentiation filter, the phase variation is changed into the intensity variation by differentiation, and then the differentiated image is integrated. In the method used in practice, the differentiated image has been recorded on a film, and then integrated by use of a filter. In this paper, however, the image differentiated by means of a filter is entered into a computer using a CCD camera and then integrated. As a result, the method provides rapid on-line processing. We have performed computer simulations and a detailed analysis of the differentiation filter and also provide the experimental results of three-dimensional image visualization.     

Multiobjective optimization method based on a genetic algorithm forswitched reluctance motor design

In this paper, a novel multiobjective optimization method based on a genetic-fuzzy algorithm (GFA) is proposed. The new GFA method is used for optimal design of a switched reluctance motor (SRM) with two objective functions: high efficiency and low torque ripple. The results of the optimal design for an 8/6, four-phase, 4 kW, 250 V, 1500 r.p.m. SRM show improvement in both efficiency and torque ripple of the motor     

Electromagnetic optimization design of a HTS magnet using the improved hybrid genetic algorithm

The genetic algorithm (GA) is an efficient method in the optimization of superconducting magnets, but there are some limitations of the GA applied to practice design of superconducting magnet, such as poor local search ability, premature convergence, etc. An improved hybrid genetic algorithm is developed by combination of the sequential quadratic programming (SQP). A high temperature superconducting (HTS) magnet by Bi-2223/Ag tape is designed through the improved hybrid GA. A new configuration of the HTS magnet which can reduce the winding volume and become more convenient to construct is suggested with consideration of the constraints, such as central magnetic filed, critical current characteristic, storage energy, and so on.     

A genetic algorithm extended modified sub-gradient algorithm for cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routes. The problem is considered in the aspect of the natural constraints of real-life production systems such as cell size, separation and co-location constraints. Co-location constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and separation constraints were included to prevent placing certain machines in close vicinity. The objective is to minimise the weighted sum of the voids and the exceptional elements. A hybrid algorithm is proposed to solve this problem. The proposed algorithm hybridises the modified sub-gradient (MSG) algorithm with a genetic algorithm. MSG algorithm solves the sharp augmented Lagrangian dual problems, where zero duality gap property is guaranteed for a wide class of optimisation problems without convexity assumption. Generally, the dual problem is solved by using GAMS solvers in the literature. In this study, a genetic algorithm has been used for solving the dual problem at the first time. The experimental results show the advantage of combining the MSG algorithm and the genetic algorithm. Although the MSG algorithm, whose dual problem is solved by GAMS solver, and the genetic algorithm cannot find feasible solutions, hybrid algorithm generates feasible solutions for all of the test problems.     

Band-notched ultra-wideband bandpass filter design using combined modified quarter-wavelength tri-section stepped-impedance resonator

This study presents a new band-notched ultra-wideband (UWB) bandpass filter (BPF) that is designed using a new structure-shared multi-mode resonator (MMR). The MMR is formed by combining two quarter-wavelength (λ/4) tri-section stepped-impedance resonators (TSSIRs) in a proper fashion to exhibit both λ/4 and half-wavelength (λ/2) types of resonance. With the first two resonant frequencies of the inherent λ/4 TSSIR, the first two of the parasitic λ/2 SIR and the coupling peaks of the input/output interdigital parallel-coupled lines properly located in the UWB passband, a six-transmission-pole UWB BPF can be realised. In this filter design, besides obtaining a uniform in-band transmission response, a notched band centred around 5.5 GHz is created by embedding L-shaped open-end slots in the TSSIR and loading the TSSIR with closely coupled winding-type resonators (WTRs) to filter out the 5-GHz wireless local-area network radio signal interferences. In addition, spur-lines are embedded in the TSSIR to sharpen the passband skirts for selectivity improvement.     

A genetic algorithm for optimal unequal-area block layout design

The use of a genetic algorithm (GA) to optimise the binary variables in a mixed-integer linear programming model for the block layout design problem with unequal areas that satisfies area requirements is analysed. The performance of a GA is improved using a local search through the possible binary variables assignment; results encourage the use of this technique to find a set of feasible solutions for the block layout design with more than nine departments.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

A modified multi-objective genetic algorithm for two-sided assembly line re-balancing problem of a shovel loader

This article deals with a real-life multi-objective two-sided assembly line rebalancing problem (MTALRBP) with modifications of production demand, line’s structure and production process in a Chinese construction machinery manufacturing firm. The objectives are minimising the cycle time and rebalancing cost, considering some specific constraints associated with the inevitable wait time, such as novel cycle time, idle time and balanced constraints. A modified non-dominated sorting genetic algorithm II (MNSGA-II) is proposed to solve this problem. MNSGA-II employs some problem-specific designs for encoding and decoding, initial population, crossover operator, mutation operator and selection operator. The great performance of MNSGA-II is demonstrated from two aspects: one is through the comparison between the representative results and current situation in the production system in terms of some ALs’ performance evaluation index, the other is utilising the comparison between the proposed MNSGA-II and two versions of initial NSGA-II in terms of ratio, convergence and spread.