Performance and Area Optimization of VLSISystems Using Genetic Algorithms

PurposeThe purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.Design/methodology/approachIn this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.FindingsThe effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.Originality/valueMost of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

This paper proposes a hybrid optimization technique combining genetic and exchange market algorithms. These algorithms are two evolutionary algorithms that facilitate finding optimal solutions for different optimization problems. The genetic algorithm's high execution time decreases its efficiency. Because of the genetic algorithm's strength in surveying solution space, it can be combined with a proper exploitation-based algorithm to improve the optimization efficiency. The exchange market algorithm is an optimization algorithm that can effectively find the global optimum of the objective functions in an efficient manner. According to the trade's inherent situation, the stock market works under unbalanced and balanced modes. In order to gain maximum profit, shareholders take specific decisions based on the existing conditions. The exchange market algorithm has two searching and two absorbent operators for acquiring the best-simulated form of the stock market. Simulations on twelve benchmarks with the different dimensions and variables prove the effectiveness of this algorithm compared to eight optimization algorithms.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

The minimization of the molecular potential energy function is one of the most important real-life problems which can help to predict the 3D structure of the protein by knowing the steady (ground) state of the molecules of the protein. In this paper, we propose a new hybrid algorithm between the social spider algorithm and the genetic algorithm in order to minimize a simplified model of the energy function of the molecule. We call the proposed algorithm by hybrid social spider optimization and genetic algorithm (HSSOGA). The HSSOGA comprises of three main steps. In the first step, we apply the social spider optimization algorithm to balance between the exploration and the exploitation processes in the proposed algorithm. In the second step, we use the dimensionality reduction process and the population partitioning process by dividing the population into subpopulations and applying the arithmetical crossover operator for each subpopulation order to increase the diversity of the search in the algorithm. In the last steps, we use the genetic mutation operator in the whole population to avoid the premature convergence and avoid trapping in local minima. The combination of three steps helps the proposed algorithm to solve the molecular potential energy function with different molecules size, especially when the problem dimension $$D>100$$D>100 with powerful performance. We test it on 13 large-scale unconstrained global optimization problems to investigate its performance on these functions. In order to investigate the efficiency of the proposed HSSOGA, we compare HSSOGA against eight benchmark algorithms when we minimize the potential energy function problem. The numerical experiment results show that the proposed algorithm is a promising and efficient algorithm and can obtain the global minimum or near global minimum of the large-scale optimization problems up to 1000 dimension and the molecular potential energy function of the simplified model with up to 200 degrees of freedom faster than the other comparative algorithms.

Optimization of the performance of flotation circuits using a genetic algorithm oriented by process-based rules

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA’s optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.

This paper presents a comparative performance assessment for loss minimization of vector controlled induction motor (IM) drive based on two different efficient optimization algorithms, namely Genetic Algorithm (GA) and Golden Search (GS). Here the features of GA and GS both for estimation and recalculation of optimized flux component of current ( ), have been utilized for a better optimal efficiency operation of the IM drive. In this context, a comparative performance of IM drive based on GA and GS optimization algorithms is carried out. Both the GA and GS based algorithms greatly improve the efficiency by reducing the core loss of the drive system. Moreover, both the approaches have no effect on parameter variation and also need no additional hardware for hardware implementation. However, GA based loss minimization scheme proves its edge over GS based scheme for the IM drive. The simulation results for various speed patterns and operating conditions are presented here. Stability study of the whole drive system is also carried out utilizing both the optimizing schemes. The performance of the proposed drive is validated experimentally on dSPACE-1104 based laboratory prototype.

The increased speed and accuracy in solving optimization problems of gas allocation in the gas lift process are of high importance. Solving gas allocation optimization problems generally involves two steps: (1) The gas lift performance curve (GLPC) fitting (gas lift modeling) and (2) optimizing the allocation of gas between wells. Therefore, in order to increase the speed and accuracy of solving gas allocation optimization problems, both steps need to be improved. In order to increase the accuracy of the first step, a new correlation was proposed in which, in addition to increasing the accuracy of fit, the optimization speed was improved by decreasing the number of constants used in the correlation. Besides, in order to improve the performance of the second step, water cycle optimization algorithm was used and the results obtained from this algorithm were compared with the results obtained from previous studies on teaching–learning-based optimization (TLBO) algorithm, continuous ant colony (CACO) algorithm, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for solving the five-well Nishikiori index problem. The results suggested that the water cycle optimization algorithm has a very good performance in terms of convergence rate, non-capture at local optimum points and repeatability. Finally, as a new problem, the gas allocation between the wells of one of the heavy oil fields in the southwest of Iran was optimized with predetermined oil production rates. The goal of optimization was to obtain the minimum amount of gas required to produce the predetermined oil rates using the water cycle optimization algorithm. The results showed that optimization is of higher importance in lower oil production targets, resulting in higher additional oil production.

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.

Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

Because of the complexity of some optimization problems, evolutionary and meta-heuristic algorithms are sometimes more applicable than the traditional optimization methods. Some difficulties in solving design-operation problems in the field of engineering are due to the multi-modality of the solution region of these problems. Since the design variables usually are specified as discrete variables and other continuous decision variables have to be set according to the range of the discrete ones, the possibility of trapping the final solution into some local optimum increases. In such cases, the capability of both traditional and evolutionary algorithms decreases. Thus, the development of a strategy to overcome this problem is the subject of this paper. For water utilities, one of the greatest potential areas for energy cost-savings is the effective scheduling of daily pump operations. Optimum design operation of pumping stations is a potential problem in this area that performs a wide background of solutions to this problem with different methods. Computation in all methods is driven by an objective function that includes operating and capital costs subject to various performances and hydraulic constraints. This paper achieves the optimal control and operation of an irrigation pumping station system by one of the latest tools used in optimization problems, which is the honey-bees mating optimization (HBMO) algorithm and is tested with a practical design. The HBMO algorithm with dynamic penalty function is presented and compared with two other well-known optimization tools which are the Lagrange multipliers (LM) method and genetic algorithms (GA) as well as with the previous results of the HBMO algorithm with constant penalty function for the same problem. The LM, GA and HBMO approaches simultaneously determine the least total annual cost of the pumping station and its operation. The solution includes the selection of pump type, capacity and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. In this paper, the HBMO algorithm is applied and the dynamic penalty function is tested to demonstrate the efficiency of this combination simultaneously. The results are very promising and prove the ability of combining the dynamic penalty function with the HBMO algorithm for solving combinatorial design–operation optimization problems. Application of all these models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and capability of the dynamic penalty function in the HBMO algorithm for solving complex problems of this type.