Usability of Honeybee Algorithms in Practice. Towards Nature-Inspired Sustainable Development
Abstract
Honeybee algorithms (HBAs), inspired by the collective intelligence of bee colonies, have garnered increasing attention for their potential to solve complex problems (especially focused on optimization aspects). This abstract delves into the practical usability of these algorithms, exploring their strengths and limitations in real-world applications, particularly within the context of sustainable development. Advantages of the approaches in problem solutions relate to efficiency (HBAs demonstrate impressive performance in finding optimal solutions, particularly for large-scale and dynamic problems), adaptability (their decentralized nature allows them to handle changes in the environment effectively, and sustainability (often require fewer resources compared to traditional optimization methods, contributing to a more sustainable approach). Some examples of implementation in sustainable development embrace resource optimization: which can optimize energy usage in buildings, transportation networks, and industrial processes, waste management: which can aid in designing efficient waste collection and recycling systems, and renewable energy integration: optimizing the placement and operation of renewable energy sources like solar panels and wind turbines.The article, aimed at popularizing bee algorithms, presents issues related to their usefulness in the context of sustainable development. Previous research related to the essence and research directions of bee algorithms, the origins and basic assumptions of these algorithms qualified as collective intelligence, and actually relating to concepts of swarm algorithms known from nature. The main part concerns the application areas and types of tasks that can be supported by such approaches. We can observe the continuous development of the original concepts of the discussed algorithms, directions of further research will be included in the summary of the article.