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This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

This paper realizes the implementation of Improved Multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing Multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a Multi-objective Optimization Problem that consists of different objective functions, viz. fuel cost with/ without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved Multi-objective Mayfly Algorithm is interpreted in terms of the present Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved Multi-objective Mayfly Algorithm, Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved Multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem.

With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies. Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and four alternatives are formed. In this study, we use Spherical Fuzzy TOPSIS method to solve the technology selection problem.

Product Development (PD) is a very crucial area for companies that want to hold a competitive and strategic advantage in the market. PD partner becomes the long-term partner; hence its selection is a critical process. In this paper, the PD evaluation and selection procedure are approached as a multi-criteria decision making (MCDM) process in which the ELICIT information-based framework is suggested. The main aim of the process is to create a flexible decision-making environment for decision-makers with linguistic expressions. The recommended methodology is tested with a case study by a Turkish agriculture firm, and the results are presented in the paper.

With the recent paradigm shift towards Cloud computing and Service Oriented Architecture (SOA), Service selection and evolution have emerged as significant challenges for service integrators and maintainers. Service selection process involves both subjective and objective factors based on user feedback and performance assessment, along with inherently imprecise data. Similarly, determining the relative importance of component services (CS) in a service composition is not a trivial task, and encounters similar challenges as for service selection. This relative importance of CSs is crucial to quantify the impact of a proposed change in service composition. However, existing literature lacks a systematic procedure which aggregates user feedback and real world performance assessment data, while incorporating inherent fuzziness of these criteria. In this study, we formulate these problems as Multi-Criteria Decision problems, and propose a hybrid MCDM model based on Fuzzy Delphi methodology (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Vikor and Fuzzy TOPSIS to tackle previously mentioned issues. The proposed model is different from existing studies in services computing, as it incorporates both user feedback and real world performance assessment data, and deals with uncertainty in decision making process. FDM is used to determine critical subjective and objective factors for service selection and importance degree of CSs in existing compositions. Then, Fuzzy AHP is used to determine the criteria weights, while FTOPSIS or FVIKOR is used to rank the alternatives.

As we search to achieve sustainable development and substance, energy security and efficiency should be viewed not only from the perspective of addressing short-term challenges, but also as a necessity for long-term growth of the economy. Latterly, some articles have majored on selecting the best energy policy and arbitrating the best energy alternatives. In this paper, AHP, VIKOR, PROMETHEE and TOPSIS approaches are suggested for the selection among energy policies. The methodology is based on these four methods under a comparison. In the application of the proposed methodologies, the best energy policy is determined for Turkey.

Service quality evaluation has become a main area of interest for practitioners, managers, researchers and policy makers, who have inclined on the passengers' service. In this paper, intuitionistic fuzzy TOPSIS approach is applied to evaluate service quality of rail transit systems of Istanbul. A total of 6553 surveys are conducted and analyzed in seven rail lines with respect to 20 service qualities.

The evaluation of medical imaging devices is a critical issue for both biomedical engineers and health-care investors. This study proposes a new technique to assess common medical imaging devices using type-2 fuzzy multi-criteria decision making approach. The evaluation criteria were characterized by the interviews with the experts. A Gaussian type-2 Fuzzy membership function was assigned for each interval of the evaluation. TOPSIS algorithm was applied to our system using type-2 Fuzzy numbers. The results were classified with the Wu and Mendel's ranking method. The ranking of device alternatives highlighted the accurate order of future imaging technologies with the fuzzy behavior of medical investments in conjunction with the requirements of the clinicians and the engineers.

In the knowledge economy, a key source of sustainable competitive advantage relies on the way to create, share, and utilize knowledge. This paper presents an application of the interval type-2 TOPSIS method used to select the most appropriate tool to support knowledge management (KM) activities in a healthcare system. The method provides us with a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner to analyze and compare KM tools in the software market. A case study is given to demonstrate the potential of the methodology.