Advancing Pollution Control through Bioinspired Optimization: A Comprehensive Review

Addressing the increasing environmental issues caused by pollution requires creative and successful optimization approaches that are both innovative and effective. Algorithms inspired by nature, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Neural Networks (ANN), offer great versatility and powerful solutions by leveraging concepts of evolution, social behavior, and neural networks. These algorithms have demonstrated remarkable results in solving dynamic and complex problems related to pollution control, such as reducing emissions, managing waste, allocating resources, and monitoring the environment. Their effectiveness in addressing various issues provides great synergies in designing sustainable approaches. Although there are still challenges, such as requiring significant computational resources or necessitating the accurate calibration of parameters, the advantages of these algorithms are numerous. This review highlights gaps in existing documents, underscoring the need to design strategies utilizing bioinspired algorithms to achieve greater efficiency in pollution alleviation, natural resource conservation, and sustainability-enhancing global efforts, there by inspiring change towards a greener world. This review emphasizes the vital role that advanced optimization techniques play in global sustainability efforts through a synthesis of current research and real-world applications. Harnessing bioinspired algorithms can result in more efficient approaches to decreasing pollution, preserving natural resources, and mitigating environmental effects. With the increasing need for sustainable solutions, bioinspired algorithms will continue to lead the way in innovation for environmental management, advancing towards a more sustainable future