Genetic Algorithms for the Optimal Design of Electromagnetic Micro-Motors

The genetic algorithm (GA) to the design of electromagnetic micro motor to optimize parameter design. Besides the different oversize from macro motor, the novel structure of micro motor which the rotor is set between the two stators make its design different, too. There are constraint satisfaction problems CSP) in the design. It is shown that the use GA offers a high rate of global convergence and the ability to get the optimal design of electromagnetic micro motors.

Topological optimization of ballistic protective structures through genetic algorithms in a vulnerability-driven environment

Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

APPROXIMATION TECHNIQUES FOR APPLICATION OF GENETIC ALGORITHMS TO STRUCTURAL OPTIMIZATION

Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

Optimal Territorial Resources Placement for Multipurpose Wireless Services Using Genetic Algorithms

This paper presents a study for finding a solution to the placement of territorial resources for multipurpose wireless services considering also the restrictions imposed by the orography of the territory itself. To solve this problem genetic algorithms are used to identify sites where to place the resources for the optimal coverage of a given area. The used algorithm has demonstrated to be able to find optimal solutions in a variety of considered situations.

A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments

Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

Optimization of perforation distribution for horizontal wells based on genetic algorithms

Early water breakthrough and a rapid increase in water cut are always observed in high- permeability completion intervals when perforations are uniformly distributed in the wellbore in heterogeneous reservoirs. Optimization of perforating parameters in partitioned sections in horizontal intervals helps homogenize the inflow from the reservoir and thus is critically important for enhanced oil recovery. This paper derives a coupled reservoir-wellbore flow model based on inflow controlling theory. Genetic algorithms are applied to solving the model as they excel in obtaining the global optimum of discrete functions. The optimized perforating strategy applies a low perforation density in high- permeability intervals and a high perforation density in low-permeability intervals. As a result, the inflow profile is homogenized and idealized.

Family genetic algorithms based on gene exchange and its application

Genetic Algorithms （GA） are a search techniques based on mechanics of nature selection and have already been successfully applied in many diverse areas. However, increasing samples show that GA＇s performance is not as good as it was expected to be. Criticism of this algorithm includes the slow speed and premature result during convergence procedure. In order to improve the performance, the population size and individuals＇ space is emphatically described. The influence of individuals＇ space and population size on the operators is analyzed. And a novel family genetic algorithm （FGA） is put forward based on this analysis. In this novel algorithm, the optimum solution families closed to quality individuals is constructed, which is exchanged found by a search in the world space. Search will be done in this microspace. The family that can search better genes in a limited period of time would win a new life. At the same time, the best gene of this micro space with the basic population in the world space is exchanged. Finally, the FGA is applied to the function optimization and image matching through several experiments. The results show that the FGA possessed high performance.

SELECTION OF OBJECTIVE FUNCTIONS AND APPLICATION OF GENETIC ALGORITHMS IN DAMPING DESIGN OF PIPE SYSTEM

The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functions for the vibration design of a pipeline or pipe system are introduced,namely,the frequency,amplitude,transfer ratio,curvature and deformation energy as options for the optimization process.The genetic algorithms(GA)are adopted as the opti- mization method,in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial.The optimization procedure for all the above ob- jective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN. The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example,and their characteristics are analyzed.Finally we put forward two new objective functions,curvature and deformation energy for pipe system optimization.The calculations show that using the curvature as the objective function can reflect the case of minimal stress,and the optimization results using the deformation energy represent lesser and more uni- form stress distribution.The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement.

Niche pseudo-parallel genetic algorithms for path optimization of autonomous mobile robot

A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem （TSP）. Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named ＂Robot tour around Pekin＂, and the performance conducted by NPPGA is better than simple genetic algorithm （SGA） and distributed paralell genetic algorithms （DPGA）.

Optimization of Linear Antenna Arrays Based on Genetic Algorithms

The methods of moment and genetic algorithm (GA) are combined to optimize the Yagi Uda antenna array and Log periodic dipole antenna (LPDA) array. The element lengths and spacing are optimized for the Yagi Uda array; while the ratio factor of spacing to length as well as the ratio of length to diameter of the elements are optimized for LPDA array. The results show that the main parameters, such as gain and pattern, have been improved apparently; and the high back lobe level of LPDA can be reduced greatly, therefore, GA is a very competent method for optimizing the linear array as well as in other fields.

Job-shop Scheduling with Multi-objectives Based on Genetic Algorithms

The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into account based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illu-strative example is given to testify the validity of this algorithm.

Application of Chaos in Genetic Algorithms

Through replacing Gaussian mutation operator in real-coded genetic algorithm with a chaotic mapping, wepresent a genetic algorithm with chaotic mutation. To examine this new algorithm, we applied our algorithm to functionoptimization problems and obtained good results. Furthermore the orbital points' distribution of chaotic mapping andthe effects of chaotic mutation with different parameters were studied in order to make the chaotic mutation mechanismbe utilized efficiently.

GENETIC ALGORITHMS AND GAME THEORY FOR HIGH LIFT DESIGN PROBLEMS IN AERODYNAMICS

A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

Splicing System Based Genetic Algorithms for Developing RBF Net-works Models

A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function(RBF)neural network,which is used to extract valuable process information from input output data.The novel RBF net-work training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity.The ef-fectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.

Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Task scheduling is the main problem in cloud computing that reduces system performance;it is an important way to arrange user needs and perform multiple goals.Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation,task scheduling,security,privacy,etc.To improve system performance,an efficient task-scheduling algorithm is required.Existing task-scheduling algorithms focus on task-resource requirements,CPU memory,execution time,and execution cost.In this paper,a task scheduling algorithm based on a Genetic Algorithm(GA)has been presented for assigning and executing different tasks.The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization.We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors.The first example contains ten tasks and four processors;the computation costs are generated randomly.The last example has eight processors,and the number of tasks ranges from twenty to seventy;the computation cost of each task on different processors is generated randomly.The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives;completion time,execution cost,and resource utilization.

Optimization of total harmonic current distortion and torque pulsation reduction in high-power induction motors using genetic algorithms

This paper presents a powerful application of genetic algorithm （GA） for the minimization of the total harmonic current distortion （THCD） in high-power induction motors fed by voltage source inverters, based on an approximate harmonic model. That is, having defined a desired fundamental output voltage, optimal pulse patterns （switching angles） are determined to produce the fundamental output voltage while minimizing the THCD. The complete results for the two cases of three and five switching instants in the first quarter period of pulse width modulation （PWM） waveform are presented. Presence of harmonics in the stator excitation leads to a pulsing-torque component. Considering the fact that if the pulsing-torques are at low frequencies, they can cause troublesome speed fluctuations, shaft fatigue, and unsatisfactory performance in the feedback control system, the 5th, 7th, 1 lth, and 13th current harmonics （in the case of five switching angles） are constrained at some pre-specified values, to mitigate the detrimental effects of low-frequency harmonics. At the same time, the THCD is optimized while the required fundamental output voltage is maintained.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.