Genetic Algorithm-based Optimization Research Articles

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

Two constitutive descriptions of boron steel 22MnB5 at high temperature

The hot deformation behavior and constitutive description of the boron steel 22MnB5 are of vital importance to the process design and numerical simulation of the steel during hot stamping. In order to establish two constitutive descriptions for 22MnB5, it was subjected to isothermal uniaxial tensile testing on a Gleeble 3500 thermomechanical simulator at temperatures ranging from 700°C to 900°C and strain rates ranging from 0.01s−1 to 10s−1. On the basis of the testing results, a strain-compensated Arrhenius-type constitutive equation and a set of unified viscoplastic equations that take into account the dislocation density were derived for describing the flow behavior of 22MnB5 during hot stamping. The material constants for the two constitutive descriptions were obtained by linear regression and genetic algorithm-based optimization, respectively. Further, the predictability of both the constitutive descriptions was assessed in terms of the correlation coefficient, the average absolute relative error, the root mean square error, and the distribution of the relative error. The results indicated that both constitutive descriptions are effective for describing the hot tensile behavior of 22MnB5, however, the prediction accuracy of the strain-compensated Arrhenius-type constitutive equation is a little higher than that of the unified viscoplastic constitutive equations.

Experimental study and genetic algorithm-based optimization of cutting parameters in cutting engineering ceramics

The paper aimed at conducting cutting experiments on engineering ceramics and investigating the influence of machining process input parameters such as cutting depth, feed speed, and tool cutting edge angle on the output parameters such as materials removal rate and cutting tool wear rate. The regression equations were then solved using the least squares fitting method and the relationships between material removal rate and input parameters as well as between cutting tool wear rate and input parameters were obtained. Genetic algorithm was available for optimizing the input parameters to get output parameters. Therefore, the optimum combinations of input parameters for maximum material removal rate and minimum cutting tool wear rate were found.

Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications

This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers’ data. The battery profiles of different manufacturers’ like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers’ catalogue) characteristics.

Development of multi-phase dynamic equations for a seven-link biped robot with improved foot rotation in the double support phase

Gait generation plays a significant role in the quality of locomotion of legged robots. This paper presents the development of multi-phase dynamic equations and optimal trajectory generation for a seven-link planar-biped robot walking on the ground level with consideration of feet rotation in the double support phase. The main contribution of this paper is to increase the stability margin at the phase transition time for simultaneous feet rotation in double support phase by introducing a new style of feet rotation. First, the derivation of the dynamics equations, which is a challenging problem due to the existence of the holonomic constraints, is performed using the Lagrangian formulation. Then, an analytical solution to inverse kinematics is proposed to determine the angles of each joint. A multi-objective genetic algorithm-based optimization technique is proposed to obtain the key parameters in trajectory generation so that the zero moment point tracks a predefined stable trajectory and additionally minimizes the power consumption, which is subjected to actuators’ powers limitations. The effect of the hip height on the total power consumption is also investigated. The numerical simulations demonstrate the effectiveness of the proposed method.

A new laminates encoding scheme for the genetic algorithm-based optimization of stiffened composite panels

Purpose – The genetic algorithm (GA) technique is widely used for the optimization of stiffened composite panels. It is based on sequential execution of a number of specific operators, including the encoding of particular design variables. For instance, in the case of a stiffened composite panel, the design variables that need to be encoded are: the number of plies and their stacking sequences in the panel skin and stiffeners. This paper aims to present a novel, implicit, heuristic approach for encoding composite laminates and, through its use, demonstrates an improvement in the optimization process. Design/methodology/approach – The stiffened panel optimization has been formulated as a constrained discrete minimum-weight design problem. GAs, which use both new encoding schemes and those previously described in the literature, have been used to find near-optimal solutions to the formulated problem. The influence of the new encoding scheme on the searching capabilities of the GA has been investigated using comparative analysis of the optimization results. Findings – The new encoding scheme allows the definition of stacking sequences in composites using shorter symbolic representations as compared with standard encoding operators and, as a result of this, a reduction in the problem design space. According to numerical experiments performed in this work, this feature enables GA to obtain near-optimal designs using smaller population sizes than those required if standard encoding schemes are used. Originality/value – The approach to encoding laminates presented in this paper is based on the original heuristics. In the context of GA-based optimization of stiffened composite panels, the use of the new approach rather than the standard encoding technique can lead to a significant reduction in computational time employed.

Experimental investigation of adhesively bonded patch repair of an inclined center cracked panel using DIC

Repair of a cracked aircraft structure by an adhesively bonded composite patch has gained lot of importance in extending the fatigue life as well as improving the structural integrity of it. In the present work, an experimental study is carried out using digital image correlation (DIC) technique to analyze the behavior of single and double-sided adhesively bonded patch repair of an inclined center cracked aluminium panel subjected to uniaxial tensile loading. Further, the shear strain distribution in the adhesive layer is also estimated using the DIC technique. Shear strain concentration at the overlap edge in the adhesive layer is very high leading to patch debonding. Further, optimum patch dimension and patch thickness for the given cracked panel are arrived using a genetic algorithm-based optimization technique in conjunction with finite element analysis (FEA). With the optimum patch configuration, a 3-D FEA is further carried out and the obtained results are compared with the experimental prediction from DIC. The DIC prediction correlates with FEA contours on overall basis.

Niching Genetic Scheme With Bond Graphs for Topology and Parameter Optimization of a Mechatronic System

This paper presents a novel multimodal evolutionary optimization algorithm for the complex problem of concurrent and integrated design of a mechatronic system, with the objective of realizing the best topology and the best parameters from a multicriteria viewpoint and with different preferences. The associated search space can be large and complex due to the existence of different classes of configurations, possible topologies, and the parameter values of the elements. The proposed algorithm efficiently explores the search space to find several elite configurations for different preferences, with more detailed competition by incorporating the domain knowledge of experts and considering some criteria that are not included in the course of regular evolutionary optimization. The developed approach consists of a two-loop optimization. For each topology, a genetic algorithm-based optimization is performed to find an elite representative of the topology. The elites will compete with each other to become the best design. A strategy of restricted competition selection is employed in the competition of topologies, with the aim of finding alternative elites from which the one that best satisfies the customer preference may be chosen. The designer may incorporate a higher level competition between elites in order to obtain the global optimum.

Genetic Algorithm-Based Optimization of Steam Consumption of Dryer Section in Paper Machine

In paper dryer section of the paper machine, paper dehydration is small, but the steam consumption for the process of dehydration is very large, this steam consumption has a great influence to the total steam consumption of the whole papermaking process. A method optimization of the dryer section based on genetic algorithm is proposed to reduce the dryer section steam consumption, which can reduce the total steam consumption of the papermaking process. After analyzing steam consumption of the whole drying process, the result is that the temperature of dryer section air is the key factor that influences the steam consumption. The model of paper machine dryer section is build with supply air temperature as a variable and steam consumption as the objective function, then using the genetic algorithm to optimize the supply air temperature. Simulation results show that after optimizing air temperature by genetic algorithm steam consumption of papermaking process is further reduced.

Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.

An intelligent methodology for optimising machining operation sequence by ant system algorithm

The paper describes an intelligent ant system-based algorithm for automatic generation of optimal sequence of machining operations required to produce a part, based on minimising the number of tool changes and set-up changes subject to satisfying all precedence constraints during manufacturing. The MATLAB programme for the algorithm uses a list of machining operations, tool approach directions, and the precedence constraints between the operations as inputs. It generates only feasible sequences of operations and finds out an optimal sequence among them. The concept of specific selection of a starting node at the beginning of each ant cycle and introducing a precedence check in the transition rules reduces the computation time significantly. A comparative study shows that for a demonstration run, the proposed ant system-based approach performed faster than previously developed methodologies for ant colony optimisation as well as a genetic algorithm-based optimisation techniques.

Combined loading of caisson foundations in cohesive soil: Finite element versus Winkler modeling

The undrained response of massive caisson foundations to combined horizontal, vertical and moment loading is parametrically investigated through a series of 3D finite element analyses. The parameters are: (a) the embedment ratio (D/B), (b) the factor of safety against initial vertical loading (FSV) and (c) the ratio of the overturning moment to the horizontal force applied at the top of the caisson (M/Q). Emphasis is given on: (i) the identification of all possible failure mechanisms in M–Q–N space, (ii) the developed stress distributions along the caisson walls for various load levels up to complete failure conditions. The results are then used as a feedback for calibrating the parameters of a generalized four-type spring model, originally proposed by Gerolymos and Gazetas (2006), through a genetic algorithm-based optimization procedure. The predictions of the Winkler model compare very well with the FE results, not only at the local response level (in terms of stress distributions along the caisson shafts), but at a global response level (in terms of force–displacement curves and M–Q–N failure envelopes at the top of the caisson) as well. Contrary to established lateral soil resistance theories, it is shown that both the ultimate horizontal soil reaction and resisting moment per unit depth do not solely depend on the strength properties of soil and geometry of the caisson but are also functions of the applied load ratio M/Q and initial soil yielding due to vertical loading. Interesting conclusions are also drawn regarding the transition from the elastic to the ultimate limit state (hardening). Quantifying through analytical expressions the contribution of each of the two basic lateral resisting mechanisms to the response of the caisson, a classification method for embedded foundations is then proposed. The capabilities of the Winkler model are further demonstrated through comparison with FE analysis of the caisson cyclic lateral response.

A Genetic Algorithm-based optimization model for supporting green transportation operations

Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.

Segment optimization of a rotating multilayer sandwich beam

This study presents the optimal distribution of cuts or segments in both the constrained and constraining layers of a rotating sandwich beam. The governing equations of motion of a rotating constrained multilayer sandwich beam is developed in the finite element form. The validity of the proposed finite element formulation is demonstrated by comparing the results with those obtained from the available literature and Ritz formulation. The significance of the number and location of the segmentation(s) on the modal damping factor is investigated by using the strain energy approach and the finite element method. An optimization problem is formulated by combining finite element analysis with genetic algorithm-based optimization technique to identify the optimal locations for the segments to achieve maximum modal damping corresponding to the first five modes of flexural vibration, individually and simultaneously. The simulations suggest that the modal damping factors are strongly influenced by the location and the number of cuts on a rotating multilayer sandwich beam.

CREDIT MANAGEMENT ANALYSIS OF SEMI-INTENSIVE SHRIMP FARMING IN MEXICO

Credit management (funding source with its specific interest rate, its timing, and amount) and its trade, affect feasibility of commercial shrimp farming. To analyze optimal trade credit management, an existing bioeconomic model of a semi-intensive shrimp farm (139 hectares with 44 earthen ponds) located in Sinaloa, Mexico, was extended to create a framework that assesses the decision-making problem related to funding from input suppliers (feed and postlarvae) during one culture cycle, integrating stochastic factors like shrimp mortality and pond water temperature. Through the application of Monte Carlo simulation and genetic algorithm-based optimization techniques, results show that the minimum funding cost is US$15,710 (for an operation budget of US$603,790), 27% lower than alternative funding scenarios (P-value <0.001). Although framed at the firm level, this approach optimizes any funding planning scenario in short-term credit management, particularly in funding schemes involving input suppliers. Further research is needed to relate credit management at the firm level with other elements of the value chain.

Reconfigurable Thinning for the Adaptive Control of Linear Arrays

Dealing with the adaptive nulling of the array radiation pattern, two reconfigurable thinning strategies are presented and assessed. An easily-reconfigurable and low-complexity antenna architecture is considered where a set of radio-frequency switches is exploited to either connect or disconnect the array elements for controlling the radiation pattern and generating deep nulls along the directions-of-arrival of the undesired signals. By defining through a genetic algorithm-based optimization the on/off status of the switches to maximize the signal-to-interference-plus-noise ratio at the antenna output, two different formulations are discussed. The first one does not constrain the number of active elements, while the other forces the solution to satisfy a fixed-directivity criterion also in correspondence with a time-varying interference scenario. The performances of the proposed approaches are assessed in both static and time-varying scenarios where single and multiple interfering signals impinge on the array from different angular directions.

Quantum simulations of the hydrogen molecule on ammonia clusters

Mixed ammonia-hydrogen molecule clusters [H2-(NH3)n] have been studied with the aim of exploring the quantitative importance of the H2 quantum motion in defining their structure and energetics. Minimum energy structures have been obtained employing genetic algorithm-based optimization methods in conjunction with accurate pair potentials for NH3-NH3 and H2-NH3. These include both a full 5D potential and a spherically averaged reduced surface mimicking the presence of a para-H2. All the putative global minima for n ≥ 7 are characterized by H2 being adsorbed onto a rhomboidal ammonia tetramer motif formed by two double donor and two double acceptor ammonia molecules. In a few cases, the choice of specific rhombus seems to be directed by the vicinity of an ammonia ad-molecule. Diffusion Monte Carlo simulations on a subset of the species obtained highlighted important quantum effects in defining the H2 surface distribution, often resulting in populating rhomboidal sites different from the global minimum one, and showing a compelling correlation between local geometrical features and the relative stability of surface H2. Clathrate-like species have also been studied and suggested to be metastable over a broad range of conditions if formed.

Accelerative Genetic Algorithm-Based Parameter Optimization in Storm Intensity Formula

The genetic algorithm is an effective method to optimize model parameters, and to improve it, will accelerate the genetic algorithm is applied to the optimization of the return period storm intensity and rainfall parameters in the relationship with the traditional regression method and preferred-dregression optimization results are analyzed and compared. An example shows that: accelerative genetic algorithm for parameter estimation in storm intensity formula accuracy than traditional regression method and preferred regression parameters meter accuracy.

Online Self-Test System Grouping Function of Genetic Algorithm-Based Optimization

The paper mainly discusses the genetic algorithm to optimize the test paper module and Research and Implementation of online self-test system. Online self-test characteristics of the system, coding, crossover and mutation improvement on traditional genetic algorithm. Experimental results show that, the improved genetic algorithm has better performance than the conventional genetic algorithm, improves the efficiency of solving the quality of test paper and issues, promotes a more widely used online self-testing system in the field of education.

Experimental modeling and optimization of electric discharge diamond face grinding of metal matrix composite

Machining of metal matrix composites (MMCs) has been a big challenge for manufacturing industries due to its superior mechanical properties. Unconventional machining methods have become an alternative to give desired shapes with intricate profiles and stringent design requirements. The present research investigates the grinding performance of copper–iron–graphite MMC using electric discharge diamond face grinding (EDDFG), which is electric discharge machining-based hybrid machining process. Experiments have been performed on a self-developed experimental setup of EDDFG with scientifically designed experiments. Effects of process input parameters on two important performances, material removal rate (MRR) and surface roughness (SR), have been analyzed. Genetic algorithm-based optimization of MRR and SR models show considerable improvements in both characteristics, as confirmed by verification experiments. Results reveal that peak current is a common significant factor for both MRR and SR.