WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems

This research aims to evaluate the performance of contract employees using the Weighted Aggregated Sum Product Assessment and Borda methods. The Weighted Aggregated Sum Product Assessment approach uses mathematical calculations, thereby producing precise and accurate results. The Borda technique is useful because it efficiently aggregates diverse rankings of alternatives through voting groups that combine many preferences. The choice of these two methodologies has the ability to produce more efficient solutions to complex problems by guiding or making decisions more quickly in resolving problems. The determining criteria for assessing the performance of contract employees are punctuality or attendance, skills, knowledge, communication, decision making, discipline, adaptation to the work environment, work safety and initiative. The results of the calculation process from the ranking combination of the weighted aggregate sum product assessment and the borda method were obtained by placing alternative 5 as an employee who is worthy of being a permanent employee and getting the highest score of 0.288. All calculations using this method can be a guide in making decisions at PT SMART Tbk Surabaya, because the accuracy of contract employee performance evaluations has a significant influence on achieving company goals in the permanent employee recruitment process.

The present article considers the problem of numerical solution of the terminal optimal control problem. The general statement of the terminal optimal control problem and a brief overview of its solving methods are presented. With a direct approach and reduction of the optimal control problem to the finite-dimensional optimization problem, the target functional on the space of desired parameters, regardless of the type of approximation of the control function, may not have the unimodal property. Therefore, it is advisable to use evolutionary algorithms to solve the problem. A general approach to solving the terminal optimal control problem of evolutionary computational algorithms is presented. The paper presents a description of some evolutionary algorithms that were selected as the most effective for solving the optimal control problem. A hybrid evolutionary algorithm based on a combination of several evolutionary algorithms is considered. The computational experiment considers the terminal optimal control problems, for which optimal solutions were found by known classical numerical methods that use the gradient of the target functionality when searching. Comparison of the results obtained by classical and evolutionary methods by functional values and computational costs allows us to conclude that evolutionary algorithms are able to effectively solve the terminal optimal control problems

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE. The proposed model presents a two-layer framework, where the WASPAS method enables a flexible scoring system by combining both the weighted sum model (WSM) and the weighted product model (WPM). On the other hand, the q-RLDFS framework facilitates high-order fuzzy modelling and accounts for hesitation in expert ratings, thereby making the decision results more interpretable and robust. To determine the effectiveness of the model, five AI algorithms named convolutional neural network-based motion analysis (CNN-MA), reinforcement learning based training optimizer (RL-TO), expert system for exercise prescription (ES-EP), hybrid AI tutor with natural language processing (HAI-NLP), and wearable sensor data mining algorithm (WSDMA) are evaluated against eight key criteria relevant to PE. Results revealed that CNN-MA is the most effective solution to implement, followed by RL-TO. The sensitivity and comparative analysis are thoroughly conducted to determine the validity of the model in terms of its robustness and reliability. The research offers distinct practical implications and actionable recommendations to educators, administrators, and policymakers, informing the strategic implementation of AI technologies within the PE domain. In general, the research makes a significant contribution to the adoption of AI in PE, as it provides a scalable, transparent, and practically applicable decision-support model.

Culinary tourism is one of the tourist attractions when visiting an area with the aim of enjoying food with a certain taste. Culinary tourism in Batu City and Malang City offers a variety of culinary products, ranging from filling food to snacks such as typical and legendary dishes. So this creates its own problems for the community and tourists because they feel confused about making choices. Therefore, there is a need for a solution to this problem, namely by creating a decision support system that can help the community and tourists in determining culinary tourism spots appropriately according to their needs. This study uses the WASPAS (Weighted Aggregated Sum Product Assessment) method to calculate which culinary tourism spots are suitable based on the needs of the user or users. Based on this research, the system calculation using the WASPAS (Weighted Aggregated Sum Product Assessment) method on a culinary tourism decision support system can provide the right recommendation for users and users by using a Likert scale calculation obtained from the questionnaire results getting a final score of 88.95 %. From the test results using Spearman Correlation, correlation values are obtained of 0.6 and 0.8 which indicate that there is a relationship between the ratings generated by the system and the calculated rankings of people who like to eat.

The best graduates are pride or people who are considered capable of selling their soft skills or hard skills after completing their Diploma III (D-III) or Starata 1 (S-1) education. The best graduate degrees selected are obtained from several criteria, such as the Grade Point Average (GPA), Organization, Achievement, Intermediate Semester / Corrective Examination, and Attitude during their education at a university. In determining the best graduate students, sometimes obstacles arise, causing difficulty in determining the best graduates. Therefore, to determine the best graduate, each alternative must have criteria. Which one, Each criterion has a weight that has been determined individually and is carried out by the completion process using the Weighted Aggregated Sum Product Assessment (WASPAS) method for the ranking process. The highest score or ranking will be used as an alternative recommendation that will get the title of the best graduate .

Conventional methods are still used in selecting the best students in the various institutions depending on the subjectivity of each member of the assigned committee. In order to make an objective decision, it is necessary to have a method that can consider the criteria used to select the candidates to be elected. The decision-making method used in this study is Weighted Aggregated Sum Product Assessment(WASPAS). This study aims to analyze the increase in accuracy of the WASPAS method that occurs in the implementation of the lambda variable in the process of combining the Weight Product Method(WPM) and Weight Sum Method(WSM). This method is use because it is suitable for the case studied where the application of this method focuses on weighting criteria with a dynamic number of alternatives and low computational complexity providing good performance in handling large amounts of data.The application of this method uses data from students from Engineering Faculty of Universitas Islam Sumatera Utara which is tested on 10 students with criteria adapted from student data attributes that can be used as parameters for decision making. The results of this study show an increase for each alternative with an average value of 23.6% for each alternative. From this study it can be concluded that accuracy is highly dependent on variations in lambda values which are affected by the determinant operator in the equation used. Therefore it is possible to find an absolute equation to give optimal effect on a single value without variation by considering the bias of the effect of the WASPAS method on the lambda variable in future research.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments.

The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.

Risk assessment based on failure mode and effects analysis (FMEA) and WASPAS methods under probabilistic double hierarchy linguistic term sets

The performance of a teacher is an outcome that is demanded by the School Principal in awarding a teacher who is at a formal level of education. In producing effective decisions on performance ratings, it is, of course, necessary to use a computer-based information system, this is known as the Decision Support System. In order for the resulting decisions to be better, using methods that can process existing data so that the method used is able to produce a final report in the form of a decision. At SD IT Al-Munadi Marelan-Medan, so far not using computer applications to help decision-makers to rank teacher performance. Although all this time the needs of the school principal are reports on the performance of teachers in schools. In the research that the authors conducted using a combination of AHP and WASPAS methods which are expected to improve the results of decisions on teacher performance ranking.

This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.

Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, and few studies have integrated machine learning with evolutionary algorithms to optimize fracturing parameters based on time-series production prediction and economic objectives. This study proposed a novel approach, combining a data-driven model with evolutionary optimization algorithms to optimize fracturing parameters. We established a significant number of static and dynamic data sets representing the geological and developmental characteristics of tight oil reservoirs from numerical simulation. Four production-prediction models based on machine-learning methods—support vector machine, gradient-boosted decision tree, random forest, and multilayer perception—were constructed as mapping functions between static properties and dynamic production. Then, to optimize the fracturing parameters, the best machine-learning-based production predictive model was coupled with four evolutionary algorithms—genetic algorithm, differential evolution algorithm, simulated annealing algorithm, and particle swarm optimization—to investigate the highest net present value (NPV). The results show that among the four production-prediction models established, multilayer perception (MLP) has the best prediction performance. Among the evolutionary algorithms, particle swarm optimization (PSO) not only has the fastest convergence speed but also the highest net present value. The optimal fracturing parameters for the study area were identified. The hybrid MLP-PSO model represents a robust and convenient method to forecast the time-series production and to optimize fracturing parameters by reducing manual tuning.