Genetic Algorithm For Optimal Design Research Articles

Optimizing a dew point evaporative cooler in data center applications

Abstract The escalating energy consumption of data centers has led to a pressing need for energy-efficient cooling solutions. This paper presents a countercurrent dew point evaporative cooler (DPEC) for data center refrigeration. We developed and experimentally validated a numerical model for DPEC, then formulated regression models using the response surface method. These models link eight key design factors, including geometrical and operational factors, to three performance indices: cooling capacity per unit volume, coefficient of performance, and outlet primary air temperature. We assessed the extent of factor influence on these indices. By using these regression models as objective functions, we used the genetic algorithm for design optimization under two climatic conditions, resulting in various optimal parameter combinations. Our findings highlight the strong predictive accuracy of these models. In comparison to the original design, the optimal design achieved an improvement of 104.8%, an increase of 23.9%, and a reduction of 13.8% in the three indices.

Modified genetic algorithm for optimal design of truss structures

In this paper, topology and shape optimization of truss or frame structures is discussed. The optimization starts from a structure, into which a finite number of nodesare set; all the nodes are connected together by trusses in all possible variants. Unfit variants of the truss system are rejected. Two alternative ways of the optimization are compared: topology optimization starting from initial structure with a larger number of nodes, and topology optimization starting from initial structure with smaller number of nodes but with additional shape optimization of the obtained topology. The topology optimization is solved with original modified genetic algorithm, giving better results in comparison with classical genetic algorithm. Instead of further development of constraint system, the additional step is introduced into algorithm – purification of genotype, which allows complementary improvement of particular population individuals, and together for the optimization process retains more possibilities than stiffening of constraints. The shape optimization is solved by classical genetic algorithm. Both strategies effectively improve the solution, however the common topology/shape optimization requires less computer resources. All numerical examples are obtained with original software developed by the authors.

Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.

Sensitivity Assessment using Genetic Algorithm for Optimal Design of RC Ring Wall Foundation of Liquid Storage Tanks

Hydrocarbons and chemical industries extensively use storage tanks made of steel for storing large quantities of liquids. These tanks are typically supported on a RC ring wall foundation. This paper presents a method to minimize the cost of RC Ring Wall Foundations and study the sensitivity of this cost towards the different design parameters. The optimization process is developed through the use of genetic algorithm which simulates the biological evolution for the fittest (optimized) organism Previous studies on use of genetic algorithm in structural engineering has been applied to different structures like frames beams, columns etc. This paper extends the use of genetic algorithm to ring wall foundations of liquid storage tanks. The objective function for optimization includes the costs of concrete, steel, formwork and excavation whose sensitivity is analysed for parameters like grade of steel, concrete, seismic and wind loading for different tank sizes. All the constraints functions are set to meet the design requirements as per Indian Standard Codes and construction industry practices. Eight cases of parametric study are considered in order to illustrate the applicability of the genetic algorithm design model. It is concluded that this approach is economically more effective compared to conventional methods for design and sensitivities of different design parameters can be quickly assessed. Additionally this design methodology can be extended to deal with other types of structures as well.

Combined flow and pressure deficit-based penalty in GA for optimal design of water distribution network

ABSTRACT The optimal design of water distribution networks involves minimizing the cost of the network subject to the constraints of satisfying the pressure requirements at all the nodes. In genetic algorithm (GA)-based optimal design, such constrained problems are generally converted to unconstrained ones using penalty methods. The penalty is applied for not meeting the pressure constraints. Researchers observed previously that the optimal solutions lie on the boundary of feasible and infeasible solutions. They suggested self-organizing penalty based on pressure heads for increasing the computational efficiency and obtaining better solutions. Usually, a flow-dependent analysis is carried out to obtain pressure deficiency at different nodes with the assumption that all nodal demands are satisfied. The assumption of demand satisfaction at all nodes for infeasible solutions results in higher pressure deficiency at different nodes, resulting in higher penalty costs than actually required. This may delay the convergence. Herein, a novel flow-based penalty approach is suggested in which the penalty is obtained based on the shortfalls in the pressures and flows at different nodes using pressure-dependent analysis. The methodology is illustrated with results from benchmark networks. The modified GA method is found to be converging faster as compared to other methods of penalty application.

Vibration absorber optimization for boom-sprayer

The main task of this work is to analyze optimal design-system of the booms of boom-sprayers. The discrete-continue models of machines dynamics of such wheeled machines as boom-sprayer with elongated boom element with the attachment of dynamic vibration absorbers are offered. The algorithms for vibration decreasing of boom are received. The new vibroabsorbing elements are proposed. The paper contemplates the provision of dynamic vibration absorbers (DVA) of buffered impact masses and particle type. Such originally designed absorbers reduce vibration selectively in maximum vibration mode, without introducing vibration in other modes. The damping results from the exchange of momentum during impacts among the masses and masses and stops as the structure vibrates. A technique is developed to give the optimal DVA’s as single degree of freedom (SDOF) buffered system. The one-digit values are established not only for the dynamic vibration absorber parameters, but also for mechanical parameter of base structure – boom in connection points of the dynamic vibration absorbers. Finally, present research develops the genetic algorithms for optimal design searching by discrete-continuum DVA’s system – base system modeling.

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Abstract A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Genetic algorithm for design optimization of multibody dynamics using differential–algebraic equation integrators

Based on the general model of design optimization of multibody dynamics, a modified genetic algorithm with adaptive crossover and mutation rates is developed to find optimal design variables which satisfy the dynamic constraints and obtain optimum objective values. Generalized-α projection method and higher order variational integrators with Lagrangian polynomial and Gauss quadrature formula are used to solve differential–algebraic equations during optimization process. Efficiency and accuracy of the numerical results obtained by intelligent design optimization with different differential–algebraic equation solving methods are compared.

A comprehensive genetic algorithm for design optimization of Z-bar loader working mechanism

The operation mechanism is generally considered as a key component of a wheel loader. Its working performance has great influence on the service life, stability, efficiency and economy of the loader. This paper establishes a mathematical model of Z-bar loader mechanism in polar coordinate with four-bar linkage and six-bar Watt linkage synthesis, investigates the working performance, such as mechanism transmission ratio, carry stability, parallelism, dumping in any position, bucket flat setting, maximum dig depth, extreme transmission angle and so on. Then we explore new design methods of joint-position of Z-bar loader linkage between tilt cylinder and loader frame to perfect some of the performance. A new method, comprehensive genetic algorithm, is presented to optimize non-linear equation with multi-constraints, and the results improve the multidisciplinary performance. Furthermore, a virtual prototype of the working mechanism is modeled to simulate and verify the optimization results.

Use of Genetic Algorithms for Optimal Design of Sandwich Panels Subjected to Underwater Shock Loading

Sandwich composite panels are increasingly used in the construction of marine vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of composite panels comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, topologies and laminate schemes. Hence, this work deals with the presentation of an optimal design of laminated composite sandwich marine structures subjected to underwater explosion. The optimization process is performed using a genetic algorithm (GA), associated with the finite element method (FEM) for the structural analysis. In this optimization procedure, sandwich composite panel finite element model is built up, then the coupled acoustic–structural arithmetic from the widely used calculation program of the finite element “ABAQUS” is used to simulate and analyze the transient dynamic response of a sandwich composite panel that experiences loading by an acoustic pressure shock wave resulting from an underwater explosion “UNDEX”. This approach is well suited for enhancing the response of orthotropic and/or laminated composites which involve many design variables. In GA method, a new approach is considered to improve this evolutionary algorithm for laminated stacking sequence and material selection of face layer and cores. Simple crossover, modified ply mutation, and a new operator called “ply swap” are applied to achieve these goals.

Specific power as objective function in GA optimal design of permanent magnet disc motor

PurposeThe application of the analysed permanent magnet disc motor (PMDM) is in electric vehicle and therefore there are several objectives that could be tackled in the design procedure, such as an increased efficiency, reduced total weight of the motor or increased power/weight ratio (specific power). The purpose of this paper is to perform an optimal design of a PMDM using specific power as objective function. In the design procedure performed on the PMDM, genetic algorithm (GA), as an optimisation tool is used.Design/methodology/approachThe created optimal design programme genetic algorithm for optimal design of electrical machines is using the GA as an optimisation tool. According to the design characteristics of PMDM, some of the parameters are chosen to be constant and some variable. A comparative analysis of both motor models based on the values of some specific parameters is performed.FindingsFrom the comparative data analysis of both motor models, it can be concluded that the main objective of the optimisation is realised, and that is achieved by a reduction of the total weight of the motor, as well as the increase of the torque, as a result of which the specific power is increased.Originality/valueAn optimisation technique based on GAs has been developed and applied to the design of a PMDM. According to the results investigated above, it can be concluded that the GA is a very suitable tool for design optimisation of PMDM and electromagnetic devices in general. The quality of the GA optimised model has been proved through the data analysis of the prototype and optimised solution. At the end, the quality of the GA solution has been again proved by comparative analysis of the two motor models using FEM as a performance analysis tool.

Improved genetic algorithm for optimal design of fuzzy classifier

One of the important issues in the design of fuzzy classifier is the formation of fuzzy if-then rules and the membership functions. This paper presents a Genetic Algorithm (GA) approach to obtain the optimal rule-set and the membership function. To develop the fuzzy system the membership functions and rule-set are encoded into the chromosome and evolved simultaneously using GA. Advanced genetic operators are applied to improve the performance of the GA in designing the fuzzy classifier. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris, Wine and tcpdump data. From the simulation study, it is found that the improved GA produces a fuzzy classifier which has minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.

A hybrid genetic algorithm for synthesis of heat exchanger networks

A new hybrid genetic algorithm for optimal design of heat exchanger networks is developed. The mathematical model used in the algorithm is based on an explicit solution of stream temperatures of heat exchanger networks with the stage-wise superstructure. By taking heat transfer areas and heat capacity flow rates as genes in the genetic algorithm, the thermal performance and total cost of arbitrary heat exchanger networks can be calculated explicitly and therefore the individuals (heat exchanger networks) can always be feasible. This characteristic makes the algorithm suitable for large scale synthesis problems. The genetic algorithm is combined with simulated annealing algorithm, local optimizing strategy, structure control strategy and other strategies so that the structural search ability of the algorithm is conspicuously enhanced. The examples presented in this work revealed special search abilities in both structure space and continuous variable space.

A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization

The high computational cost of population based optimization methods, such as multi-objective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective∕constraint functions) calls. We present a new multi-objective design optimization approach in which the Kriging-based metamodeling is embedded within a MOGA. The proposed approach is called Kriging assisted MOGA, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points are evaluated on-line using Kriging metamodeling instead of the actual simulation model. The decision as to whether the simulation or its Kriging metamodel should be used for evaluating a design point is based on a simple and objective criterion. It is determined whether by using the objective∕constraint functions’ Kriging metamodels for a design point, its “domination status” in the current generation can be changed. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average K-MOGA converges to the Pareto frontier with an approximately 50% fewer number of simulation calls compared to a conventional MOGA.

Design considerations for passive gamma-ray spectrometers

Passive gamma-ray spectrometers composed of attenuation filters and integrating detection materials provide important advantages for measurements in high-radiation environments and for long-term monitoring. Each of these applications has requirements that constrain the design of the instrument, such as incident energy range of interest, sensor size and weight, readout method, and cost. The multitude of parameters in passive spectrometer design (e.g., attenuation filter material and thickness, integrating sensor type, number of pixels, reconstructed energy bin structure) results in a large design space to examine. The development of generalized design optimization tools to interrogate this space and to identify promising spectrometer designs is discussed, particularly the methods used to rapidly calculate system transfer functions and the use of genetic algorithms for design optimization. Preliminary measurements to validate the design tools are described, and example results from early design optimization efforts are provided.

Multiobjective GA Optimization Using Reduced Models

In this paper, we propose a novel method for solving multiobjective optimization problems using reduced models. Our method, called objective exchange genetic algorithm for design optimization (OEGADO), is intended for solving real-world application problems. For such problems, the number of objective evaluations performed is a critical factor as a single objective evaluation can be quite expensive. The aim of our research is to reduce the number of objective evaluations needed to find a well-distributed sampling of the Pareto-optimal region by applying reduced models to steady-state multiobjective GAs. OEGADO runs several GAs concurrently with each GA optimizing one objective and forming a reduced model of its objective. At regular intervals, each GA exchanges its reduced model with the others. The GAs use these reduced models to bias their search toward compromise solutions. Empirical results in several engineering and benchmark domains comparing OEGADO with two state-of-the-art multiobjective evolutionary algorithms show that OEGADO outperformed them for difficult problems.

Improved genetic algorithm for design optimization of truss structures with sizing, shape and topology variables

This paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are proposed, including binary and float coding, integer and float coding. Surrogate function is applied to unify the constraints into single one; moreover surrogate reproduction is developed to select good individuals to mating pool on the basis of constraint and fitness values, which completely considers the character of constrained optimization. This paper proposes a new strategy of creating next population by competing between parent and offspring population based on constraint and fitness values; so that lifetime of excellent gene is prolonged. Because the initial population is created randomly and three operators of GA are also indeterminable, it is necessary to check whether the structural topology is desirable. An improved restart operator is proposed to introduce new gene and explore new space, so that the reliability of GA is enhanced. Selected examples are solved; the improved numerical results demonstrate that the enhanced GA scheme is feasible and effective. Copyright © 2005 John Wiley & Sons, Ltd.

On Maximizing Solution Diversity in a Multiobjective Multidisciplinary Genetic Algorithm for Design Optimization

This article presents a new method for multiobjective multidisciplinary design optimization. The method obtains an estimate of the Pareto frontier with maximum solution diversity using a quality index, referred to as entropy index. Unlike previous methods that maintain diversity in the solution set heuristically, our method improves overall quality of solutions by explicitly optimizing the entropy index at every system-level iteration, and then using this information to bias the search process toward obtaining a solution set with maximum diversity. Our method utilizes a multiobjective genetic algorithm as an optimizer in each subproblem of a multidisciplinary optimization problem. To demonstrate the proposed approach, we applied our method to a mechanical design problem of a speed reducer and the results are compared with those obtained by a few other multiobjective optimization methods. A minimal set of quality indexes is used to compare the diversity and optimality of the obtained solution sets from the different methods on a quantitative basis.

Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products

A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.