Real Coded Research Articles

Use of Real Coded Genetic Algorithm as a Pre-Dimensioning Tool for Prestressed Concrete Beams

In project practice, the search for optimal solutions is based on the traditional process of trial and error, which consumes much time and does not guarantee that solutions found are the optimal solutions for the problem. Many studies have been developed in recent years with the aim of solving problems in various fields of structural engineering with the aid of intelligent algorithms; however, when it comes to the optimization of structural designs, the approaches considered by the authors involve a large number of variables and constraints, making the implementation of optimization techniques difficult and consuming significant processing time. This research aims to evaluate the efficiency of intelligent algorithms when associated with structural optimization approaches that are simpler to implement. Therefore, a Genetic Algorithm in Real Coding was built to serve as an auxiliary tool for pre-dimensioning prestressed concrete beams. With this, the problem becomes simpler to implement, as it depends on a smaller number of variables, leading to less processing time consumption. Simulations were performed to calibrate the Genetic Algorithm and find the optimal solution later. The solution found by the algorithm was compared with the real solution of a project that had already gone through a traditional optimization process. Even in these circumstances, the proposed Genetic Algorithm was able to find, in 210 s, a more economical solution. Our studies found that even with more straightforward approaches, intelligent algorithms can help in the search for optimal solutions to structural engineering problems; in addition, using real coding in fact proved to be a great strategy due to the nature of the problem, making the implementation of the algorithm simpler and ensuring answers with little processing time.

A Hyper Heuristics Technique for Data Partitioning and Scheduling to Heterogeneous Systems using Genetic Algorithm and Improved Particle Swarm Optimization

Development of the load partitioning for multiple round load distribution and effective scheduling of partitioned load to heterogeneous processor is primary goal of distributed and parallel system. In this paper, we propose hyper heuristics scheduling algorithm for load partitioning using genetic and improved particle swarm optimization techniques.A communication model is used to predict the optimal activation order, optimal number of processor and optimal number of rounds of the load. Heuristics Based Scheduling Algorithm is proposed using Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) to form Scheduling Solutions (heuristics algorithms) for large scale system with diversity operator as sequence dependent and sequence independent scheduling. For this solution, processing time of the entire processing load will be reduced. Hybrid Real Code genetic algorithm(HRGA) computes optimal activation order with cross over and mutation operator without considering the processor latency and different types of variation in the perturbation parameters. In order to optimize this issue, we utilizeImproved Particle swarm optimization (IPSO) determine the load fraction for generating activation order in terms of dynamically predicting fitness value of the processor with certain number. The Simulation analysis demonstrates the proposed model performance in terms of mean, standard deviation, computational complexity and Average Execution Time comparing against hybrid real coded genetic algorithm

Sizing, Shape, and Topology Optimizations of Roof Trusses Using Hybrid Genetic Algorithms

Structural optimizations have received great attention from structural engineers. Several optimization methods have been proposed including evolutionary strategies and genetic algorithms. This paper considers hybrid genetic algorithms for roof truss optimizations. Practically, roof truss optimizations are unique. In this case, the pitch angles are usually governed by roof covering types. In the optimization process, the pitch angle is set to constant, while the coordinates of the joints are determined by genetic algorithms. The optimization process utilizes hybrid genetic algorithms, i.e., a combination of binary and real coded genetic algorithms. Genetic algorithms are optimization methods that have been used successfully for various problems. For the sizing, shape and topology optimizations considered in this paper, the area of cross section and the number of members connected to every node are optimized using binary coded genetic algorithms, while the coordinates of the nodes are determined using real coded genetic algorithms. The optimization process for binary and real coded algorithms is done subsequently. The use of real coding for joint coordinates of structures gives the program the flexibility to obtain the final position of the joints. The arithmetic crossover is used to tackle this matter. In every generation, a portion of new individuals is inserted randomly replacing the old individuals. This can be considered to increase the variability of the population. In addition, the fittest individual is always transferred into the next generation. The penalty to the individuals that are violating the constraint is set to a minimum fitness in this paper. It can be shown that the proposed procedure is able to obtain the optimum design of roof truss structures.

Identification of maximum loadability limit and weak buses using security constraint genetic algorithm

Maximum loadability limit (MLL) is a realistic index to evaluate the steady state voltage stability because it provides system operator a better practical sense of security margin in the aspects of engineering parameter like system loading. If MLL is identified, load margin, voltage stability, security margin can be determined and precaution can be taken. Conventional power flow methods like Newton–Raphson, Gauss–Siedel, fast decoupled power flow methods suffer to provide proper MLL under security constraints as Jacobian matrix becomes singular when system loading approaches its loadability limit. Hence evolutionary techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) are now-a-days applied to solve the non-linear complex MLL problem. Phase angles, active power, voltage magnitudes of load buses, reactive power of PV buses are considered as security constraints for MLL problem. New simple real coded Security Constraint GA (SCGA) is developed to solve the problem. MLL problem is formulated as maximization problem. As handling of real coded power flow variables are easier than binary coding, real coding of SCGA parameters is applied. Novel formulas are developed to update power flow parameters considering corresponding power mismatches. Utilizing decoupling properties of power system, mutation is implemented. To provide diversity, new parent selection in crossover section is adopted. Weak buses are also identified for the application of FACTS devices. The developed method is compared with general PSO (GPSO) technique for test systems of IEEE 14, 30, 57 and 118 bus. Showing characteristics and results, the effectiveness and efficiency of the proposed method is established.

Genetic Algorithms (Binary and Real Codes) for the Optimisation of a Fermentation Process for Butanol Production

In this work, the capability of genetic algorithms (GAs) to optimise an alternative fermentation process for the production of biobutanol was assessed. The process consists of three interconnected units, as follows: fermentor, cell retention system (tangential microfiltration) and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The dynamic behaviour of the process is described by a non-linear mathematical model with kinetic parameters determined experimentally, whose non-linearity makes the solution of the optimisation problem difficult through conventional algorithms, thus justifying the use of an evolutionary method based on the GAs. The objective of the optimisation was the search of the process inputs that maximises the productivity of butanol for a desired substrate conversion. The potential of binary and real coded genetic algorithms to solve the optimisation problem was assessed. The GA parameters were evaluated making use of the statistical technique of the factorial design in order to identify the most significant ones to the GAs response and to determine the values of the parameters that improve the GAs performance. With both GA codes similar solutions to the optimisation problem were obtained. However, in relation to computational time, the binary code outperformed the real code. The optimised process ran on concentrated sugar solution (140.7 g/l), reaching a high final butanol concentration (27.1 g/l) and high butanol productivity (9.0 g/l.h). The use of mathematical optimisers in the butanol fermentation is a novel approach and sums up the efforts of recent researches in turning the biobutanol industry commercially viable.

Binary versus real coding for genetic algorithms: A false dichotomy?

The usefulness of the genetic algorithm (GA) as judged by numerous applications in engineering and other contexts cannot be questioned. However, to make the application successful, often considerable effort is needed to customise the GA to suit the problem or class of problems under consideration. Perhaps the most basic decision which the designer of a GA makes, is whether to use binary or real coding. If the variable of the parameter space of an optimisation problem is continuous, a real coded GA is possibly indicated. Real numbers have a floating-point representation on a computer and the decision space is always discretised; it is not immediately evident that real coding should be the preferred method for encoding this particular problem. We re-visit this, and other decisions, which GA designers need to make. We present simulations on a standard test function, which show the result that no one GA performs best on every test problem. Perhaps the initial choice to code a problem using a real or binary coding is a false dichotomy. What counts are the algorithms for implementing the genetic operators and these algorithms are a consequence of the coding. References D. Rani and M. M. Moreira. Simulation--optimization modeling: A survey and potential application in reservoir systems operation. Water Resources Management. Springer Netherlands, 2009. http://www.springerlink.com/content/p61p535r2277r852/ John H. Holland. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. Ann Arbor: University Michigan Press, 1975. http://mitpress.mit.edu/catalog/author/default.asp?aid=3235 Z. Michalewicz. Genetic algorithms + data structures = evolution programs. 3rd edition, New York: Springer--Verlag. http://www.springer.com/computer/ai/book/978-3-540-60676-5 D. Fogel. Evolutionary computation: toward a new philosophy of machine intelligence. 3rd edition, IEEE Press, 2006. http://ebooks.ebookmall.com/ebook/232338-ebook.htm C. Macnish. Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation. Connection Science, 19:4, 2007, 361--385. http://www.springerlink.com/content/2446104674981574/

Modifications of real code genetic algorithm for global optimization

A series of modifications to the real coded genetic algorithm for the task of locating the global minimum of multidimensional functions are presented here. These modifications include a new stopping rule, a novel mutation mechanism and a periodically application of a local search procedure. The proposed modifications are tested on a series of optimization problems and the results are reported.

Coefficient Estimation of IIR Digital Filters Using a Real-Coded Genetic Algorithm

This paper proposes a methodology to estimate the system coefficients for the infinite impulse response (IIR) digital filters using real code GA. In the traditional real coded GA, it adapts the general genetic operations, whereas in this paper the proposed real coded GA applies improved genetic operations in order to search the optimal solution in given problems. Each of unknown IIR digital coefficients collected as forms of a chromosome. Two illustrative examples including the band pass and band stop IIR digital filters are demonstrated to verify the proposed method.

PERFORMANCE OF HYBRID REAL CODED GENETIC ALGORITHMS

This paper presents a new robust, two -phase hybrid optimization algorithm to solve non-linear constrained optimization problems. The proposed algorithm is very much useful when addressing heavily constrained optimization problem in terms of solution accuracy and computation time. It can out perform conventional genetic algorithms (CGAs) is the sense that Hybrid GAs makes it possible to improve both the quality of the solution and reduce the computing expenses. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local search method is next employed to do fine tuning. The phase - 1 uses real coded genetic algorithm, while optimization by direct search and systematic reduction of the size of search region method is employed in the phase - 2. Thirteen test problems were taken from the literature have been simulated and results obtained were compared. The results clearly demonstrate that the proposed hybrid real coded genetic algorithm not only improve the reliability but also make the algorithm more efficient in terms of number of function evaluations to reach the global optimum point. Thus the usefulness and effectiveness of this algorithm is placed in a sharper focus.