As a consequence of the kinetic energy changes of nature's building components, such as wind, solar, hydrogen, and water, nations have begun to embrace renewable energy sources to meet their rising energy needs. This perspective has also boosted innovation, creativity, research and development, and practicality. With the expansion of research into renewable energy generation, solutions that limit environmental damage are being further explored. This chapter discusses the difficulty of selecting a location for the marine energy production plant. Thus, a comprehensive hybrid approach was introduced to evaluate and rank several candidate locations for a marine renewable energy plant. The hybrid approach includes two multicriteria decision-making methods, the fuzzy multiobjective optimization method by ratio analysis method and the fuzzy preference ranking organization method for enrichment evaluation II method. The hybrid methodology is conducted in a fuzzy environment. In this regard, triangular fuzzy numbers are used to complete study procedures and assessments.

Site selection is a crucial element in the construction of photovoltaic solar plants since it determines future electricity-generating capacity and socioeconomic advantages. Using a multicriteria decision-making (MCDM) method, the major objective of this chapter is to analyze and choose the optimal site for utility-scale solar photovoltaic projects. The model takes into account several factors, including technical, economic, environmental, and sociopolitical considerations, with the objective of maximizing power production while reducing project expenses. In this regard, a comprehensive approach consisting of two decision-making methods, the entropy method and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, is applied. The approach is applied under the fuzzy environment with triangular fuzzy numbers. In this regard, the entropy method is used to prioritize the main aspects and subfactors while the VIKOR method is used to evaluate and rank the selected locations.

Using renewable energy is seen as an effective method for energy saving and emission reduction in the context of low-carbon economic development. In light of the intermittent and variable nature of renewable energy, the selection of the appropriate energy storage technology among a variety of choices is crucial for accelerating the growth of renewable energy. It is usually difficult for a stakeholder to choose the most sustainable technology out of the many energy storage technologies. Therefore, in this chapter, a comprehensive approach is presented to prioritize energy storage technologies and select the optimal technology. The presented approach consists of two multicriteria decision-making methods, the Simple Multiattribute Rating Technique (SMART) method and the Multiattributive Border Approximation area Comparison (MABAC) method. The steps of the approach under the fuzzy environment are presented. First of all, the SMART method is used to prioritize four main perspectives and sixteen subindicators. Then, the MABAC method is used to evaluate and rank the five sustainable energy storage technologies.

The selection of renewable energy sources for a location relies on a number of contradictory criteria, such as economic, technical, and social factors. Determining the optimal RE sources is often seen as a crucial decision-making process. In this context, multicriteria decision-making (MCDM) could be the best option for dealing with such perplexing scenarios. This chapter examines a real case study by applying MCDM techniques to indicate appropriate RE sources for Egypt. Five RE sources (i.e., geothermal, hydroelectric, solar, wind, and bioenergy) have been evaluated, and the optimal one is selected with the help of the TOPSIS method and the AHP method, used to identify the weights of the criteria (e.g., economic and environmental) and their subcriteria (e.g., capital cost and waste disposal). From the analysis, the results show that solar energy sources would be a suitable option for harnessing energy.

Bioenergy is a kind of renewable energy that has the ability to contribute to a wide range of social, environmental, and economic goals, as well as help in the process of sustainable development. Because of a lack of accurate and complete data, the evaluation, administration, and monitoring of the many bioenergy production technology possibilities are complicated in nature and give distinct advantages. Choosing the right technology for the production of bioenergy, however, presents a number of important problems and unknowns owing to the many different aspects involved. Therefore, the main objective of this chapter is to evaluate bioenergy production technologies and select the most sustainable alternative. A comprehensive two-method approach to multicriteria decision-making was introduced under the fuzzy environment. In the presented approach, the multiobjective optimization method by ratio analysis method is applied to evaluate the main and subfactors that influence the selection of the best alternative. Then, the complex proportional assessment method is applied to evaluate and rank the selected alternatives according to their importance and sustainability.

Multiple benefits have been made by hydrogen, one of the most potential players in the future energy system for a higher quality of life; it is thus essential to find the most sustainable technology for manufacturing hydrogen among the commercially available technologies. In this chapter, a comprehensive multicriteria decision-making approach to prioritizing hydrogen production technologies under uncertainty is presented. The approach consists of two decision-making methods, the CRiteria Importance through Intercriteria Correlation (CRITIC) method—the Combinative Distance based Assessment (CODAS) method. In the approach, the CRITIC method is applied to determine the weights and priorities of the 5 main sides and their 18 subsides by capturing each of the uncertain subjective judgments regarding the importance of the various sides. Also, the CODAS method is applied to evaluate five hydrogen production technologies to verify the feasibility of the approach used under an illustrative case study.

There are many obstacles, such as administrative and policy, to the deployment of renewable energy technology. This chapter discusses barriers and solution techniques to advance renewable energy technology through technical analysis. Particularly, five policies are discussed to address four barriers and sixteen subbarriers using two multicriteria decision-making methods: the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. These methods were introduced to evaluate the policies for overcoming the barriers under a fuzzy environment based on the experts' opinions. In the evaluation process, renewable energy barriers and their subbarriers are assessed using the DEMATEL method, while five sustainable policies are examined using the TOPSIS technique. From the analysis, it is observed that "Training and capacity building" is the most feasible policy to overcome the barriers to accelerate renewable energy implementation.

Because of the promotion of sustainable development strategies, there has been significant interest in wind energy project developments during the last decades. As a result, the number of wind energy projects has expanded significantly, making wind energy an essential component of an integrated power system. The success of an offshore wind energy project depends on the selection of the ideal site for an offshore wind power plant (OWPP), which is often decided through multicriteria decision-making (MCDM). In this chapter, an approach is introduced to evaluate and rank a group of candidate sites for the construction of an OWPP. The presented approach consists of two MCDM methods: the entropy method, used to prioritize main dimensions and their subindicators, and the evaluation based on distance from average solution method, which is used to rank the candidate sites. The approach is presented in a fuzzy environment using triangular fuzzy numbers.

Comprehending the energy transition is of paramount importance in order to ascertain forthcoming business, societal, and ecological trajectories. The forthcoming economic, environmental, and social shifts will be contingent upon the manner in which energy policy shapes the process of energy transition and adjusts to interrelated transformations. This chapter has the purpose to introduce some of the most significant challenges and problems that arise when trying to use renewable energy for sustainable development. It also presents the advantages and disadvantages of the multi-criteria decision-making methods that were used in the study and selection of the best renewable energy sources in the previous chapters. Multi-criteria decision-making methods compare and rank alternative energy sources based on a range of criteria, such as technical feasibility, economic viability, environmental impact, and social acceptability.

Feature selection (FS) plays a vital role in minimizing the high-dimensional data as much as possible to aid in enhancing the classification accuracy and reducing computational costs. The purpose of the FS techniques is to extract the most effective subset features, which might enable the machine learning (ML) algorithms to better grasp the input data’s patterns and improve their classification performance. Although several metaheuristic algorithms have been recently presented to solve this problem, they still suffer from several disadvantages, such as getting stuck in local optima, slow convergence speed, and a lack of population diversity, which prevent them from achieving the desired solutions in an acceptable time. Therefore, this study is presented to propose a new feature selection approach, namely OBMSASA, based on integrating the recently published mantis search algorithm with the opposition-based learning (OBL) method and simulated annealing (SA) to strengthen its exploration and exploitation operators. The OBL method aims to improve the exploration operator, making the algorithm able to avoid stagnation into local minima; meanwhile, the SA is used as a local search to further strengthen the exploration operator, thereby improving the convergence speed. The K-nearest neighbor algorithm is used to compute the accuracy of the selected feature. The proposed algorithm is assessed using 21 common datasets and compared to several rival optimizers in terms of several performance metrics, including convergence curve, average fitness, computational cost, length of selected features, and standard deviation, to observe its effectiveness and efficiency. The source code is publicly accessible at https://drive.mathworks.com/OBMSASA.

Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the V-shaped transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively.

Energy has been characterized as a "strategic commodity," and any uncertainty regarding its supply poses a danger to the operation of the economy, especially in emerging nations. Every civilization needs energy to satisfy its fundamental requirements. Sustainable socioeconomic development requires an inexpensive and reliable energy supply with minimal environmental consequences and greenhouse gas emissions. Renewable energy (RE) forms play a crucial role in combating climate change by delivering sustainable and clean electricity. As a result of technology improvements, a broader awareness of RE, and government assistance through favorable supporting policies, RE forms are evolving to satisfy energy needs in a cleaner manner. This chapter examines the potential, obstacles, and related challenges associated with RE developments. Taking care of them will result in sustained social and economic growth. It also provides a brief overview of the various RE sources.

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Big data refers to large, diverse, and complicated data sets that are challenging to store, analyze, and visualize for use in subsequent operations or outcomes. Exploring and analyzing vast amounts of data in order to find significant patterns and principles is called data mining. Data mining is crucial to many human endeavors because it uncovers previously undiscovered patterns that are helpful. There are several main tasks of data mining, including Clustering, feature selection, and association rules. Several data mining techniques are employed to handle these significant duties. Metaheuristic algorithms are currently regarded as one of the most efficient methods for handling data mining issues. Black boxes like metaheuristics can offer distinct solutions regardless of the problem's nature. These algorithms treat data mining problems as combinatorial optimization problems. Numerous research papers are published in this area each year, which is why we decided to give a survey study on the topic. Consequently, this paper provides a thorough literature review on using metaheuristic algorithms to solve data mining issues that have emerged in the last five years (2019-2023).

We propose an integrated IoT model to blend IoT technologies, neutrosophic theory and AHP to handle uncertain conditions of real-life situations and aid decision-makers with systematic and optimum decisions. In our case study, four ranked scenarios are assigned the appropriate IoT technology generated to support the government and competent authorities in the pandemic outbreak to prevent growing risks. Our study is based on the decision-makers' judgments that need to be expanded with more experts in the various aspects of government and competent authorities. The integrated IoT model provides a balance between the restart of economic life and COVID-19 outbreaks.

The evaluation of hydropower projects in terms of sustainability standards is a multifaceted and complicated problem. When making decisions on energy planning, it is necessary to take into account not just economic factors but also those pertaining to technology, the environment, and society. In the subject of energy planning, the use of multicriteria decision-making (MCDM) methodologies has become standard practice as a result of the flexibility afforded by the ability to assess many criteria and goals concurrently. This chapter presents a comprehensive and integrated MCDM approach to the evaluation and site selection of sustainable hydropower projects. The approach consists of two decision-making methods, the criteria importance through intercriteria correlation method and the measurement of alternatives and ranking according to the compromise solution method. The approach used is applied under a fuzzy environment. In addition, the approach is applied using the triangular fuzzy numbers.

The use of robots in various stages of the production process is now commonplace across practically all sectors of the economy. Additionally, even for present-day small and medium-sized businesses, this has developed into a very powerful need in recent years and continues to grow in importance. The selection of an industrial robot is a very complicated decision-making issue due to the fact that there are numerous aspects and criteria that are in conflict with one another, as almost all of the earlier research emphasized. In addition, the many sophisticated requirements that have been added to these robots by the makers of robotics have led the level of complexity to expand even further. As a result, decision-makers are faced with increasingly complex decision-making difficulties that are influenced by a great deal of uncertainty. As a result of this, a combined neutrosophic multi-criteria decision-making (MCDM) approach may assist in resolving a significant number of the ambiguities that are suggested in the present article. Initially, the Entropy method was used to evaluate the criteria set for the study under the neutrosophic environment. Then, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was used to evaluate and rank five robots used in the automotive industry. The results indicate that the criteria of performance and working accuracy are the most influential criteria in choosing the most appropriate robot. Also, the results indicate that the KAWASAKI robot is the best choice in the manufacturing process for the automotive industry.