Hybrid Genetic Algorithm Optimization Research Articles

Fundamental kinetics model of acidity-activity relation for ethylene oligomerization and aromatization over ZSM-5 zeolites

The kinetics of ethylene oligomerization and aromatization (OA) over ZSM-5 with different Si/Al ratios was modeled by applying single-event concept combined with Brönsted kinetic model to establish a quantitative acidity-activity relationship. The NH3 desorption activation energy was related to the activation energy of the OA reactions and heat of de/protonation based on linear free-energy theory by introducing γ and δ as kinetics parameters for assessing the sensitivity of the OA reactions to the acid strength. A total of 36 parameters was estimated for the subtype elementary steps in each kinetics model. A hybrid genetic optimization algorithm was generated to obtain the kinetics model parameters, where the model can efficiently and accurately fit the changes in the product distribution obtained with the three catalysts for products in the reaction network. The regressed kinetics model can predict the influence of zeolites with the same topology and different acidity on the product distribution.

Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient

The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.

A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems

Workflow scheduling is a key component behind the process for an optimal workflow enactment. It is a well-known NP-hard problem and is more challenging in the heterogeneous computing environment. The increasing complexity of the workflow applications is forcing researchers to explore hybrid approaches to solve the workflow scheduling problem. The performance of genetic algorithms can be enhanced by the modification in genetic operators and involving an efficient heuristic. These features are incorporated in the proposed Hybrid Genetic Algorithm (HGA). A solution obtained from a heuristic is seeded in the initial population that provides a direction to reach an optimal (makespan)solution. The modified two fold genetic operators search rigorously and converge the algorithm at the best solution in less amount of time. This is proved to be the strength of the HGA in the optimization of fundamental objective (makespan) of scheduling. The proposed algorithm also optimizes the load balancing during the execution side to utilize resources at maximum. The performance of the proposed algorithm is analyzed by using synthesized datasets, and real-world application workflows. The HGA is evaluated by comparing the results with renowned and state of the art algorithms. The experimental results validate that the HGA outperforms these approaches and provides quality schedules with less makespans.

Reliability-based design optimization of broadband microwave absorbers

This paper proposes a reliability-based design optimization approach to handle variability/uncertainties involved in the design variables/parameters of microwave absorber. The proposed approach uses hybrid genetic algorithm for optimization and Monte Carlo simulation with Latin hypercube sampling technique for probabilistic analysis to identify the optimal design of absorbing structure under probabilistic constraints. The proposed approach is illustrated with examples considering broadband absorbing coatings in the frequency range of 0.2–2, 2–8, and 0.5–8 GHz, respectively.

A Hybrid Genetic Algorithm for Constrained Optimization Problems

Abstract —Genetic algorithm (GA) is a powerful method to solve constrained optimization problems (COPs). In this paper, a new fitness function based hybrid genetic optimization algorithm (NFFHGA) for COPs is proposed, in which a new crossover operator based on Union Design is presented, and inspired by the smooth function technique, a new fitness function is designed to automatically search for potential solutions. Furthermore, in order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. Experiments on 6 benchmark problems are performed and the compared results with the best known solutions reported in literature show that NFFHGA can not only quickly converge to the optimal or near-optimal solutions, but also have a high performance.

Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks

A structure optimization of concrete hollow brick with four rectangle enclosures is carried out to minimize the equivalent thermal conductivity (ETC) in the constraint of variable shape and position parameters. During the optimization hybrid genetic algorithm (HGA) is developed combining with artificial neural networks (ANN). The modified Latin hypercube sampling (i.e. the maximum minimum distance criterion) is employed to make a robust decision. The ETC of the samples is computed using the finite volume method (FVM) on the basis of 3D multi-mode heat transfer simulation. It indicates that the well-trained ANN can accurately predict the ETC of the concrete hollow brick which matches very well with data obtained from the FVM simulation. The optimization obtains 21.69% improvement on the ETC for the given range of design parameters. The optimized concrete hollow brick owns the largest void volume fraction, the minimum rid and wall thickness, same width of the enclosure, and the optimum staggered arrangement with two same large enclosures and two same small enclosures, which is resulted by the multi-mode heat transfer characteristic of the concrete hollow brick. A novel method of the optimum concrete hollow is proposed to construct new concrete hollow brick with many rows of enclosures. Relative Staggered Ratio (RSR) is used to discuss the effect of the staggered form. By combining two or more rows of the optimized enclosures to one brick with the same size the efficiency to block heat transfer is evidently improved. It is concluded by the present work that the combination of ANN and HGA and the popularizing method are powerful to the optimization of the concrete hollow brick.

Fuzzy modeling and hybrid genetic algorithm optimization of virus removal from water using microfiltration membrane

Membranes are finding increasing applications in disinfection processes including virus removal from water for municipal effluent reuse. The capability of virus removal from water by microfiltration membranes has previously been demonstrated. In this study, the capability of fuzzy logic for modeling and simulation of dead-end microfiltration process for removal of IBR and FMD viruses from water was elucidated. The main parameters indicating membrane performance i.e. flux and rejection were experimentally obtained under different conditions and compared with theoretically calculated flux and rejection using fuzzy inference system. The genetic algorithm which is an efficient and systematic method was employed in the design of fuzzy model for optimization of the poorly understood, irregular and complex membership function with improved performance. Hybrid genetic algorithm was used for optimizing the parameters that are located at the Gaussian membership functions in the premise and consequent of each rule. The results indicated that fuzzy inference system predicts the key parameters i.e. flux and rejection for different operating conditions with an acceptable error. In other words FIS is able to apply for modeling the microfiltration membrane which is mathematically difficult or in many cases an unpredictable process.

A Hybrid Genetic Algorithm for Optimization of Two-dimensional Cutting-Stock Problem

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfil the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.