Application Of Real-coded Genetic Algorithm Research Articles

Evaluation of Varied Model Order in GA-optimised Parameter Estimation of Toothbrush Rig System

Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resemble the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. Real- coded genetic algorithm indicates that parameter estimation with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others.

Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.

Application of Real-Coded Genetic Algorithm in Ship Weather Routing

Solving the problem of ship weather routing has been always a goal of nautical navigation research and has been investigated by many scientists. The operation schedule of an oceangoing ship can be influenced by wave or wind disturbances, which complicate route planning. In this paper, we present a real-coded genetic algorithm to determine the minimum voyage route time for point-to-point problems in a dynamic environment. A fitness assignment method based on an individual's position in the sorted population is presented, which greatly simplifies the calculation of fitness value. A hybrid mutation operator is proposed to enhance the search for the optimal solution and maintain population diversity. Multi-population techniques and an elite retention strategy are employed to increase population diversity and accelerate convergence rates. The effectiveness of the algorithm is demonstrated by numerical simulation experiments.

Improved Transient Performance of a Fuzzy Modified Model Reference Adaptive Controller for an Interacting Coupled Tank System Using Real-Coded Genetic Algorithm

The main objective of the paper is to design a model reference adaptive controller (MRAC) with improved transient performance. A modification to the standard direct MRAC called fuzzy modified MRAC (FMRAC) is used in the paper. The FMRAC uses a proportional control based Mamdani-type fuzzy logic controller (MFLC) to improve the transient performance of a direct MRAC. The paper proposes the application of real-coded genetic algorithm (RGA) to tune the membership function parameters of the proposed FMRAC offline so that the transient performance of the FMRAC is improved further. In this study, a GA based modified MRAC (GAMMRAC), an FMRAC, and a GA based FMRAC (GAFMRAC) are designed for a coupled tank setup in a hybrid tank process and their transient performances are compared. The results show that the proposed GAFMRAC gives a better transient performance than the GAMMRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient performance for the control of nonlinear processes.

Numerical analysis of non-Newtonian TEHL line contact problem based on real-coded genetic algorithm

This paper presents a new simple numerical method to solve the non-Newtonian thermal elastohydrodynamic lubrication (TEHL) line contact problem based on real-coded genetic algorithm (RGA). To solve the line contact TEHL problem by means of RGA, the initial population producing method is deeply investigated, an appropriate grid partition method is proposed and an appropriate evolutional computation technology is determined. Finally, the fitness evaluation function and the iterative stop qualifications are put forward. The algorithm is examined by some examples, and the results are compared with empirical solutions. The study shows that the application of RGA in the numerical analysis of the TEHL line contact problem is feasible, reliable and sufficiently accurate.

Real-coded genetic algorithm for system identification and controller tuning

This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First GA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also, cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in nonlinear pH process. The simulation results show that the GA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system.

Thermal Synthesis of Bismuth-Doped Yttrium Iron Garnet for Magneto-Optical Imaging

This contribution deals with the preparation and characterization of bismuth-doped yttrium iron garnets with composition BiY2Fe5O12 (Bi:YIG). The samples have been prepared by sintering of metal oxides homogenized in acetone for 10 minutes. The estimated average crystallite size of the single phase Bi:YIG synthesized at temperature 950 °C for 15 hours is ~ 65 nm. The real content of Bi cations in samples was quantitatively evaluated by application of real-coded genetic algorithm to the powder X-ray diffraction patterns.

An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy

The purpose of this research work is to solve mixed-integer non-linear programming problem with constraints by a real-coded genetic algorithm (RCGA). This GA is based on Roulette wheel selection, whole arithmetic crossover and non-uniform mutation. Here, mutation is carried out for the fine-tuning capabilities of the system by non-uniform operator whose action depends on the age of the population. This methodology has been applied in solving multiple price break structure and implemented for multi-item deterministic inventory control system having two separate storage facilities (owned and rented warehouse) due to limited capacity of the existing storage (owned warehouse). Also, demand rate is a linear function of selling price, time and non-linearly on the frequency of advertisement. The model is formulated with infinite replenishment and shortages are not allowed. The stocks of rented warehouse (RW) are transported to the owned warehouse (OW) in bulk-release rule. So, the mathematical model becomes a constrained non-linear mixed-integer problem. Our aim is to determine the optimal shipments, lot size of the two warehouses (OW and RW), shipment size and maximum profit by maximizing the profit function. The model is illustrated with numerical example and sensitivity analyses are performed with respect to different parameters.