OPTIMUM DESIGN OF BUTTRESS DAM USING GENETIC ALGORITHM

The chapter covers two main areas, these being an introduction to the technology and techniques associated with genetic algorithms and then the second part looks at how genetic algorithms can be used to search for good topological solutions to engineering design challenges. The start of the chapter places genetic algorithms in context compared to other evolutionary algorithms and also describes the reasons why genetic algorithms are potentially useful. This is then followed by a look at the concept of a search space. Section two looks at the canonical genetic algorithm as a basic introduction to the technology and includes an examination of the main techniques used to encode the genome, fitness functions, operators and selection. Section three looks at how genetic algorithms can be used for design and chooses the specific example of the conceptual design of commercial office buildings. Section four introduces the basic concepts of topological search and explains how having the right form of representation is vital before looking at example relating to structural components and the design of domes using a genetic algorithm linked to computational geometry techniques. The final section then looks at further methods using generative representations and generative geometries as possible solutions to the need to develop powerful forms of representation for handling topological search in genetic algorithms.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

This paper examines the viability of using the combination of the vortex lattice method for aerodynamic performance prediction with a genetic algorithm for the optimization of the aerodynamic performance of horizontal axis wind turbine blades. The work described in this paper includes the adaptation of a vortex lattice code designed to predict propeller performance to wind turbine performance prediction and the optimization process including results for both single point and multipoint design optimization efforts. Background The economics of deploying large wind turbine farms as a substantial source of electrical power is driven in large part by the efficiency of power conversion from wind energy to rotational mechanical energy. In the 1920’s, Betz formulated the basic analysis for the limiting case for horizontal axis wind turbine efficiency and set up the guidelines for how windturbine efficiencies should be calculated. A modern explanation of the Betz analysis can be found in Reference 1. In the 1930’s Glauert applied classical aerodynamic methods to airplane propeller designs in an effort to optimize performance of the horizontal axis machine for propulsion. 2 In the 1970’s and 1980’s blade element and momentum theory models were developed and refined for modeling wind turbine performance 3-5 and the efficiency of these models made them ideally suited for the genetic algorithm optimization work performed in the 1990’s by Selig et al. 6 This method for optimizing horizontal axis wind turbines using genetic algorithms used an improved version of the momentum theory models and demonstrated a successful and efficient optimization strategy. Hampsey 1 improved upon the optimization efforts by using a B-spline approach for modeling the blades along with a panel method for performance prediction. In this important optimization using a relatively higher order method for aerodynamic prediction, the blade geometries were not modeled using traditional airfoil shape theory. In the development of performance prediction for sails, it has been shown that the prediction of aerodynamic loads using the vortex lattice method is often much more accurate than load predictions based on simpler momentum theory methods. As with the panel methods, computational efficiency necessary for optimization can be maintained with the vortex lattice method. 7 A vortex lattice method has been applied to ship propellers 8 and to airplane propellers and has been shown to accurately predict performance in appropriate applications. 9,10 The airplane propeller analysis reported in

Resorting to the superposition principle of mutual inductance, multi-antiparallel coil (MAC) is able to obtain stable mutual inductance as the separation between transmitting and receiving coils changes, which is attractive for range-stable wireless power transfer. This paper presents a new method for the optimal design of MAC. The status of every coil is denoted by a ternary code, and the status code is optimized by a genetic algorithm (GA) to maximize a properly designed fitness function. The global searching ability of GA allows for achieving better design performance than the point matching design method. Besides, with the aid of ternary coding, the GA searching space is limited, and the optimization effort can be reduced.

Steel–concrete hybrid towers (SCHTs) have been adopted as a popular supporting structure for wind turbines with the increasing hub height and rotor diameter. The excessive transverse vibration of the supporting tower adversely compromises the turbine’s operation and leads to structural deteriorations. The tuned mass damper (TMD) has been validated as an efficient device integrated with high-rise structures in vibration suppressions. This study aims to provide a reliable method for determining the key parameters of the TMD to obtain the optimal performance in vibration suppressions. This study begins with a rigorous theoretical analysis to investigate the dynamic responses of the SCHT. The dynamic analysis serves as a basis for developing a dynamics model of the SCHT integrated with a TMD, which provides a programmable method to analyze the effect of the TMD on the dynamic responses of the SCHT. Then, an optimization model based on the genetic algorithm (GA) is established to obtain the optimal parameters of the TMD. Finally, a numerical test comprising 12 loading conditions is conducted to analyze the vibration mitigation effect of the TMD. The validation process also provides discussions about the influence of the wind turbine operation states and the fitness function adopted in the GA on the efficiency of the TMD. Those findings demonstrate the effectiveness and limitations of the TMD for mitigating vibrations of the SCHT. It thereby contributes to the understanding and improvement of vibration control strategies for SCHT structures and can guide future design and optimization efforts.

Abstract: This paper is about the optimization of a small circular loop antenna using a Genetic Algorithm (GA) to significantly enhance its radiation efficiency for operation at 38.22MHz. Traditional small loop antennas are known for their poor radiation characteristics due to extremely low radiation resistance and high conductor losses, rendering them unsuitable for practical RF communication without a complex matching network. This research applies a Genetic Algorithm implemented in MATLAB to optimize two critical design parameters: the loop radius and the wire radius (thickness). An analytical electromagnetic model was developed to compute radiation resistance, conductor loss resistance (accounting for skin effect and radiation efficiency. The objective function was designed to maximize efficiency by minimizing its negative value. Results from the GA were analysed using convergence plots and two-dimensional contour maps, revealing the efficiency distribution across the design space. The optimized antenna achieved a loop radius of 0.02458m and a wire radius of 0.0099m, leading to a dramatic improvement in radiation efficiency from 0.02% (traditional design) to 99.4%. Additionally, gain and bandwidth performance were significantly improved, making the antenna suitable for terrestrial RF applications. The study demonstrates that evolutionary optimization techniques like GA can overcome the inherent limitations of traditional small loop antennas, by intelligently navigating the design space, the Genetic Algorithm identified configurations that would be difficult to obtain through manual tuning. These findings highlight the practical potential of computational intelligence in modern antenna design particularly for compact efficient and broadband systems in wireless and communication technologies.

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed. The genetic algorithm (GA) adopted is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this symbiotic-evolution-based fuzzy controller (SE-FC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SE-FC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SE-FC have been verified from this problem and from comparisons with the traditional GA-based fuzzy systems.

Cable laying design is one of the most complex and tedious tasks in the design of substation power plants. Manual design will consume a lot of manpower. At the same time, the traditional two-dimensional design cannot truly represent the design scene, and it is prone to design errors for collision checking. Compared with traditional design methods, substation power plant design based on 3D design software is more intuitive and accurate, and has been widely used in the industry. However, no matter what the design method is, the cable laying path algorithm has always been dominated by traditional algorithms, which are often limited by complex designs and large data requirements. This paper mainly studies the optimal routing path problem of cable laying in 3D design scenarios. In this paper, AutoCAD software is used to establish a three-dimensional cable design system for substations. Using this system to design cable laying paths can reduce labor design costs. The cable path optimization module of the system uses genetic algorithm (GA) to optimize the laying path. This paper compares the laying optimization results of the basic GA and the improved GA. Compared with the basic GA, the improved GA improves the laying capacity by increasing the algorithm operation time.

Development and validation of a hybrid aerodynamic design method for curved diffusers using genetic algorithm and ball-spine inverse design method

Thermal and integral lifting analysis of a lattice shell lighting roof based on genetic algorithm and probabilistic design

A key challenge for the future is to reduce drastically the human impact on the environment. In the aeronautic field, this challenge aims at optimizing the design of the aircraft to decrease the global mass. This reduction leads to the optimization of every part constitutive of the plane. This operation is even more delicate when the used material is composite material. In this case, it is necessary to find a compromise between the strength, the mass and the manufacturing cost of the component. Due to these different kinds of design constraints it is necessary to assist engineer with decision support system to determine feasible solutions. In this paper, an approach is proposed based on the coupling of the different key characteristics of the design process and on the consideration of the failure risk of the component. The originality of this work is that the manufacturing deviations due to the RTM process are integrated in the simulation of the assembly process. Two kinds of deviations are identified: volume impregnation (injection phase of RTM process) and geometrical deviations (curing and cooling phases). The quantification of these deviations and the related failure risk calculation is based on finite element simulations (Pam RTM® and Samcef® softwares). The use of genetic algorithm allows to estimate the impact of the design choices and their consequences on the failure risk of the component. The main focus of the paper is the optimization of tool design. In the framework of decision support systems, the failure risk calculation is used for making the comparison of possible industrialization alternatives. It is proposed to apply this method on a particular part of the airplane structure: a spar unit made of carbon fiber/epoxy composite.

Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms

In the process of multi-objective problems, traditional algorithms have certain defects, especially because of the increasing number of conflicts and the increasingly complex application environment. This paper proposes a practical method to solve the multi-objective optimization problem for the duration and cost requirements in the construction project management, in order to reduce the error probability that the optimized project will produce in the actual project. Different from the existing optimization methods, this design modifies the curve of the time cost, and improves the parameters of the traditional genetic algorithm design and operation process design, which greatly increases the degree of fitness between the algorithm and the actual project. The model is based on genetic algorithms and combines different algorithms, so a simple and excellent Pareto solution can be quickly obtained. At the same time, optimization and simulation are carried out in an orderly way during the construction period. This new method is reasonable and effective in the very complicated multi-objective optimization problem of managing construction period and cost through an engineering example.

Facility layout is an important aspect of designing any manufacturing setup. However, the problem of finding optimal layouts is hard and deterministic techniques are not computationally feasible. In this work a genetic algorithm is presented for obtaining efficient layouts. The different aspects involved in the design of efficient genetic algorithms are discussed in detail. It is shown that the population maintained by the genetic algorithm for facility layout should consist of feasible solutions only. A new efficient crossover operator is developed. Experimental results obtained with the proposed algorithm on test problems taken from the literature are promising.

SHRNUTÍ Tento članek popisuje metodu automatickeho navrhu siluety vozu. V průběhu historie vzniklo mnoho automobilů jejichž rysy jsou do značne miry podobne. Tyto rysy (siluety) můžeme extrahovat pomoci statisticke metody analyzy hlavnich komponent a použit je pro navrh nove siluety, ktera je dale optimalizovana pomoci genetickych algoritmů. Proces optimalizace zohledňuje zadane fyzikalni parametry jako je objem prostoru pro motor, zavazadla a pasažery. Vysledek produkovany algoritmem pak slouži jako inspirace pro člověka-designera a může byt zobecněn na vybrany typ produktu.