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Metaheuristic optimization algorithms have become one of the most important research areas for solving complex, nonlinear, and high-dimensional optimization problems. These algorithms are widely used in many application domains such as engineering, artificial intelligence, energy systems, healthcare, communication technologies, manufacturing, data science, and industrial applications. Their strong exploration and exploitation capabilities enable them to provide effective solutions for optimization problems where classical optimization methods are insufficient. In addition, many metaheuristic optimization algorithms attempt to establish a balanced and adaptive search behavior between exploration and exploitation processes, reflecting an important form of search symmetry during the optimization process. This systematic review presents a comprehensive analysis of metaheuristic optimization algorithms based on 515 distinct metaheuristic optimization algorithms. In this study, the algorithms are systematically classified into eleven major categories according to their inspiration sources and dominant search behaviors: Evolutionary-Based, Swarm Intelligence-Based, Bio-Inspired, Ecological/Environmental-Based, Physics-Based, Mathematics-Based, Human-Based, Game/Sport-Based, Chemical and Molecular-Based, Music-, Art-, and Culture-Inspired, and Hybrid and Advanced Metaheuristics. The review analyzes the historical evolution, inspiration diversity, search structures, symmetry-oriented search balance, and development trends of modern metaheuristic optimization algorithms. This study also provides a broader understanding of how metaheuristic optimization algorithms have evolved structurally and conceptually over time. In addition, the study highlights the importance of balanced and adaptive search mechanisms for maintaining optimization efficiency under complex problem conditions. The findings show that the number and diversity of metaheuristic optimization algorithms have increased significantly, especially after 2015. Population-based search structures remain the dominant optimization approach in the literature, while hybrid optimization methods, adaptive search mechanisms, and artificial intelligence integration have become major research trends in recent years. The study also discusses important challenges in the field, including algorithm similarity, benchmark standardization, theoretical limitations, and fair performance evaluation.

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies.

With the growing global demand for clean energy, photovoltaic (PV) systems have become an important part of modern renewable energy infrastructures. However, their nonlinear behavior, sensitivity to irradiance and temperature variations, and complex operating conditions require advanced optimization and control methods. In this context, Marine Predators Algorithm (MPA) has attracted increasing attention due to its flexible search capability and balanced exploration–exploitation structure. This review presents a systematic analysis of MPA-based approaches applied to PV energy systems. A total of 124 studies were examined, considering only works in which PV systems are the main research focus and MPA is used as a core optimization, estimation, control, or energy management tool. The literature is classified into nine application domains: PV parameter estimation, PV modeling techniques, maximum power point tracking (MPPT) studies, MPPT under partial shading, PV control systems, grid-connected PV systems, PV system design and sizing, PV energy management systems, and advanced PV optimization applications. Comparative insights are provided based on performance indicators such as convergence behavior, tracking performance, root mean square error (RMSE), stability, robustness, efficiency, and cost-related metrics. The findings show that standard MPA remains the most widely used approach, while hybrid and modified variants are increasingly proposed to improve accuracy, convergence, and adaptability. The review also highlights that most studies are simulation-based, whereas real-world data and experimental validations remain limited. Overall, this study provides a structured perspective on current trends, practical limitations, and future research directions in MPA-based PV optimization.

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting.

Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have been widely applied in modeling and prediction tasks. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), a well-established AI approach, was employed for the time-series forecasting of Brent oil prices. To ensure effective learning and improve prediction accuracy, ANFIS was trained using nine different metaheuristic algorithms: Artificial Bee Colony (ABC), Selfish Herd Optimizer (SHO), Biogeography-Based Optimization (BBO), Multi-Verse Optimizer (MVO), Teaching–Learning-Based Optimization (TLBO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Marine Predator Algorithm (MPA), and Flower Pollination Algorithm (FPA). Symmetric training procedures were applied across all algorithms to ensure fair and consistent evaluation. The analyses were conducted on the lowest and highest daily, weekly, and monthly Brent oil prices. Mean squared error (MSE) was used as the primary performance metric. The results showed that all algorithms achieved effective prediction performance. Among them, BBO and TLBO demonstrated superior accuracy and stability, particularly in handling the complexities of Brent oil forecasting. This study contributes to the literature by combining ANFIS and metaheuristics within a symmetric framework of experimentation and evaluation.

Crude oil is one of the most important assets that are used in the production of many industrial products in a wide variety of areas. The importance of crude oil has made it important to predict its future price. Therefore, it is possible to come across many studies in the literature in which the price of crude oil is estimated in the short or long term. In this study, innovative adaptive neuro-fuzzy inference systems (ANFIS) based approaches are proposed to estimate the daily minimum and maximum prices of crude oil. The used data was taken from the period between January 3, 2022, and December 29, 2023. A total of 516 different days of data were collected to create the dataset for analysis. For daily forecasting, time series data were transformed into a data set consisting of two inputs and one output. Moth-flame optimization algorithm (MFO), flower pollination algorithm (FPA), biogeography-based optimization (BBO) and artificial bee colony (ABC) were used in training ANFIS. The results obtained in the training and testing processes were compared. When the results obtained were compared, it was shown that the relevant algorithms were effective in the daily estimation of crude oil. It has been observed that effective results are also achieved at low evaluation numbers, especially thanks to the fast convergence feature of the MFO and BBO algorithms.

The process of learning a programming language is seen as complicated, especially for those who first start to learn programming language. For this reason, visual programming tools have begun to be used in educational environments. Visual programming tools purpose to concretize abstract programming language concepts. Alice program is one of the visual programming tools. The aim of this study is to determine the effects of the Alice program on programming learning in the education environment. The study group was 18 freshman students enrolled in the Object Based Programming I course at the vocational school computer technology department. The study lasted 8 weeks, with 4 hours each week. The teaching was realized on Gagne’s nine events of instruction model. At the end of the teaching process, two focus group interviews were conducted with the students to determine the obstructing and facilitating factors in using the Alice program. This study was utilized a descriptive case study. The data were analyzed by using the content analysis. When the results were examined; It was seen that the Alice program makes learning the basic code concepts easy and understanding programming logic, and increases the desire to learn programming. However, it was stated that the practicality of writing code could not be improved because code could not be written in the Alice program. It is thought that employing the Alice program is beneficial for enhancing comprehension of programming logic.

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers.

Bu çalışmanın amacı alan yazına ve uzmanların görüşüne dayalı olarak etkileşimli elektronik kitap tasarım ilkelerini belirlemek, bu ilkeler esas alınarak altıncı sınıf sosyal bilgiler dersinin bir ünitesi için etkileşimli bir e-kitap geliştirmek, geliştirilen e-kitap ile MEB tarafından yayınlanan örnek e-kitabın öğrencilerin akademik başarısı üzerindeki etkisini karşılaştırmak ve kitaplarla ilgili öğrenci görüşlerini belirlemektir. Bu amaca ulaşmak için öncelikle alan yazın taranmış ve e-kitap tasarımında dikkat edilmesi gereken özellikler belirlenmiştir. Sonrasında sosyal bilgiler alanında uzman öğretmen ve öğretim elemanlarıyla, bilgisayar ve öğretim teknolojileri (BÖTE) alanında uzman öğretim elemanları ve lisansüstü öğrencilerle yarı yapılandırılmış görüşmeler yapılmıştır. Görüşmelerde MEB tarafından hazırlanan kitap değerlendirilerek e-kitabın öğretim açısından etkili bir şekilde tasarlanması için sahip olması gereken özellikler tespit edilmiş ve ilgili alan yazın ile görüşme sonuçları analiz edilerek etkileşimli e-kitap özellikleri belirlenmiştir. Belirlenen özelliklere dayalı olarak etkileşimli bir kitap geliştirilmiştir. Geliştirilen kitap alan uzmanlarının görüşüne sunulmuş ve kitaba son hali verilmiştir. Kitaplar, eğitim ortamında basılı kitap yerine kullanılarak öğrencilerin akademik başarısına etkisi belirlenmiştir. Öğretim süreci sonunda öğrencilerin kitaplara yönelik görüşleri alınmıştır. Deney ve kontrol gruplarının genel başarı testi ve başarı testi bilgi düzeyi puan ortalamaları arasında anlamlı bir farklılık görülmezken, başarı testi kavrama düzeyi puan ortalamaları arasında anlamlı farklılık görülmüştür.

In this study, a hybrid method based on the marine predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented to identify nonlinear systems exhibiting symmetrical or asymmetrical behavior. In other words, the antecedent and conclusion parameters of the ANFIS are adjusted by the MPA. The performance of the MPA is evaluated on eight nonlinear systems. The mean squared error is used as the error metric. Successful results were obtained on the eight systems. The best mean training error values belonging to the eight systems are 1.6 × 10⁻⁶, 3.2 × 10⁻³, 1.5 × 10⁻⁵, 9.2 × 10⁻⁶, 3.2 × 10⁻⁵, 2.3 × 10⁻³, 1.7 × 10⁻⁵, and 8.7 × 10⁻⁶. In the ANFIS training carried out to solve the related problems, the performance of the MPA was compared with the butterfly optimization algorithm, the flower pollination algorithm, moth–flame optimization, the multi-verse optimizer, the crystal structure algorithm, the dandelion optimizer, the RIME algorithm, and the salp swarm algorithm. The results have shown that the performance of the MPA mostly outperforms other algorithms in both training and testing processes.