Image compression involves reducing the size of image data file, while retaining necessary information.This paper uses the facilities of the Genetic Algorithm for the enhancement of the performance of one of the popular compression method, Vector Quantization method is selected in this work. After studying this method, new proposed algorithm for mixing the Genetic Algorithm with this method was constructed and then the required programs for testing this algorithm was written. The proposed algorithm was tested by applying it on some image data files. Some fidelity measures are calculated to evaluate the performance of the new proposed algorithm. A good enhancement was recorded for the performance of the Vector Quantization method when mixed with the Genetic Algorithm. All programs were written by using Matlab (version 7.0) and these programs were executed on the Pentium III (866 MHz) personal computer.
Designing large structures like dams requires carefully selecting various geometric, hydraulic, and structural characteristics. The required structural design and performance criteria are considered when selecting these characteristics. In order to find the best solution, a variety of restrictions must simultaneously be carefully taken into account. This study presents an effective method for determining the optimal shape design for concrete buttress dams. The research was divided into two crucial phases. The dam's initial design and subsequent modeling were mostly done using DIANA FEA and traditional design and stability analysis. After that, a genetic algorithm was used on the MATLAB platform to control optimizing the dam's shape. Three design factors were used in this phase to alter the goal function and to reduce the amount of Concrete used, which decreased project costs. These variables covered three areas of the buttress's cross-section. Two important limitations were scrutinized during this optimization process: establishing a safety margin against overtopping and preventing sliding. The analysis included a detailed assessment of Shear friction stability to complete a thorough stability study. The optimization efforts had a spectacular result, resulting in a significant 52.365% reduction in the total volume of Concrete used, dropping from 19147.5 cubic meters to 9122.55 cubic meters. This decrease was made possible by reducing three distinct components (X1, X2, X3), with respective proportions of 37.5%, 13.33%, and 30%, including two segments related to the buttress and the final segment linked (slab) to the strip footing.
The most popular evolutionary search techniques are genetic algorithms (GAs). Even though they are frequently used to solve control engineering problems, they are currently not a common tool in the control engineer's toolbox. This may be due in part to the fact that there are currently few general overviews of the employment of GAs for control engineering problems, and that they are often reported on at computer science conferences rather than conferences for control engineers. This review study is intended to assist researchers and practitioners in identifying prospective research issues, potential solutions, as well as advantages and disadvantages of each technique. This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems. Additionally, offers a number of control techniques used with the GA that have undergone extensive research. The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn’t used till the time of preparing this review.
Responding quickly and economically to the diversification of customer needs has forced manufacturing companies adopting approaches to delivering low cost, high quality sustainable products based on finding a link between the design or the manufacturing processes and other key elements of sustainability; economic, environmental, and social. However, these approaches had limited success. The most likely reason for the lack of integration between the design and manufacturing stages of the product and complexity of addressing the above mentioned three key elements of sustainability due to existing of many variables in relation to design, manufacturing, locations, logistic operations and so on. Taking into account the required integration as well as the associated complexity of considering sustainability elements can lead to large space alternative solutions and it is more difficult to use only exact methods to the optimization of such problem. This paper presents a genetic algorithm (GA) approach aiming to optimize a high sustainability performance by designing a product and the corresponding manufacturing processes for that product. Process optimization is carried out in terms of the highest fitness function achieved where different objectives are to be optimized simultaneously. The proposed GA approach is applied to the industrial case example. The proposed approach can assist decision makers to help explain when justifying their decision on what are the best product design and its manufacturing processes to obtain high sustainability performance.
The convergence of cloud and edge computing in smart manufacturing offers significant potential for improving efficiency in Industry 4.0. However, task scheduling in this context remains a complex, multi-objective challenge. This study introduces a novel Cloud-Edge Smart Manufacturing Architecture (CESMA), leveraging a hybrid approach that integrates NSGA-II and the Improved Monarch Butterfly Optimization (IMBO) algorithms. The combination utilizes NSGA-II's global search and non-dominated solution capabilities with IMBO's fine-tuning and local optimization strengths to enhance task scheduling performance. Where CESMA combines the scalability and analytics power of cloud computing with edge-based real-time decision-making to address the dynamic demands of smart manufacturing. Through extensive simulations and experiments, the feasibility and effectiveness of CESMA are validated, showing improved task scheduling quality, resource utilization, and adaptability to changing conditions. This research establishes a robust platform for managing the complexities of task scheduling in cloud-edge environments, advancing intelligent manufacturing processes, and contributing to the integration of evolutionary algorithms for real-time industrial decision-making
Indeed, there are many hydrology variables influence on the operating of dam and reservoir system. Thus, modelling of dam operation is a complicated issue due to the nonlinearity of such hydrological parameters. Hence, the identification of a modern model with a high capacity to cope with the operation of the dam is extremely important. The current research introduced good an optimization algorithm, namely Genetic Algorithm (GA) to find best operation rules. The main aim of the suggested algorithm is to minimize the difference between irrigation demand and water release value. The developed algorithm was applied to find operation rules for Timah Tasoh Dam, Malaysia. This research used significant evaluation indexes to examine the algorithms' performance. The results indicated that the GA method achieved low Vulnerability, high Resilience and Reliability. It has been demonstrated that the GA method will be a promising tool in dealing with the problem of dam operation.