Metaheuristic Optimization Review

Forecasting carbon dioxide emissions from vehicles is essential for sustainable transportation, climate-aware urban planning, and intelligent environmental management, as road transport contributes substantially to energy-related greenhouse gas emissions. Rapid growth in vehicle ownership, mobility demand, fuel use, freight activity, and congestion has exposed the limits of conventional emission-estimation methods, particularly when nonlinear and multi-factor relationships must be represented. This review examines how artificial intelligence and machine learning support vehicle carbon dioxide emission forecasting through data-driven models, deep learning, feature selection, hyperparameter optimization, and metaheuristic algorithms. It emphasizes the value of integrating traffic volume, vehicle attributes, fuel type, speed patterns, road conditions, temporal behavior, weather variables, and energy-consumption indicators to improve prediction reliability under real-world conditions. Optimized forecasting models can support emission monitoring, mitigation planning, traffic management, and low-carbon mobility policies, although challenges remain in data availability, interpretability, uncertainty, regional transferability, computational cost, and transparent validation.

Air pollution is a critical environmental issue that threatens almost the world, and public health, ecosystems, and the sustainability of cities are affected by the severe impacts of air pollution. Urbanization and industrialization have been on the run, with escalating pollution levels. Hence, air monitoring and air quality prediction are necessary for such challenges. This review discusses advanced machine learning (ML), deep learning (DL) techniques, and IoT-based study hybrid frameworks for air-quality prediction in urban settings. Integration of different data sets such as meteorological parameters, concentrations of pollutants, and data from satellite imagery, these technologies provide strong and scalable solutions for real-time monitoring and forecasting. Some of the advancements include the use of IoT-enabled sensors, the use of convolutional and recurrent neural networks, and the development of location-specific predictive models. Despite significant evolution, several challenges of data sparsity, computational requirements, and model adaptability remain. This paper casts the technologies as transforming cities into smart and green cities and advancing the cause for continuous innovation and interdisciplinary collaboration to strengthen their effectiveness. These findings add to the advancement of knowledge on air quality prediction methodologies and their crucial role in sustainable urban development.

This paper reviews the design and integration of hybrid energy systems (HES) as a solution to solve the challenges of renewable energy integration. It emphasizes the role of optimization algorithms in improving system performance, reducing costs and enhancing environmental sustainability by effectively managing energy supply and demand. Recent advances in energy estimating, machine learning, and accurate resource forecasting demonstrate significant system flexibility and efficiency increases. Furthermore, it identifies existing challenges, such as scalability, high initial costs and integration complexities, while proposing future research and innovation pathways. The study argues that HES can revolutionize energy systems and contribute to global sustainability goals by addressing key gaps.

Metaheuristic optimization algorithms become essential to solving structural design problems because they can handle nonlinear, multiple-mode, large-scale, and other difficulties. This review focuses on how MOAs have been developed and utilized and how they have compared efficiency in structural engineering design optimization. It describes some of the main milestones, such as hybrid and ensemble algorithms, as well as quantum annealing and finite elements, to improve the accuracy of the results. The study organizes and assesses modern approaches scientifically and accentuates their benefits and pitfalls in practical applications. Hypotheses derived from benchmarking and statistical exercises show that enhanced MOAs are reliable and fast in yielding almost ideal structures within a manageable computational frontier. Finally, the review outlines the limitations of the current research and suggests research foci for the future advancement of metaheuristic methods and their use in structural engineering optimization.

This review aims to identify metaheuristic optimization and machine learning in the context of network management in the current era and some graphs of real network applications, such as traffic prediction, resource assignment, and network protection. Bio-inspired meta-functions, which model heuristic approaches to problem-solving in nature, have been shown to provide the best solutions to the OP problem and possess properties that make them ideal for optimizing dynamic networks. In the same vein, neural networks and reinforcement learning models have also performed significantly better in optimizing network performance by providing precise forecasts and decision-making adaptabilities. Incorporating these methodologies into folded working models has facilitated the development of solutions for the more complicated new networks such as SDNs, MANETs and IoTs. This review consolidates the most recent work in this field while identifying new advances as revolutionary technologies for refining the next-generation networks; it discusses possible paths for future research to overcome the existing drawbacks.