From nature to computation: Bio-inspired optimization techniques and applications

In recent years, various fields, including biology, mathematics, and computer science, have begun to use bio-inspired optimization techniques. When dealing with optimization issues, these methods emerge. Because of the difficulty of solving multi-objective optimization issues, scientists are looking to bio-inspired algorithms for a possible solution. This review paper looks at ten cutting-edge bio-inspired optimization algorithms, breaks them down into their contributions to bio-inspired computation and optimization, and reveals their advantages and disadvantages. Furthermore, it explores opportunities for substantial future study in the optimization domain and provides a bibliometric analysis of pertinent literature based on the Scopus databases. This paper delves into the idea of self-organization in bio-inspired algorithms and how they have been used in many studies. Along the way, we identify significant flaws that need further investigation, which will help future studies and bio-inspired optimization progress. Optimization strategies that draw inspiration from biological principles have recently become increasingly popular within computer science, mathematics, and biology, thanks to their capacity to provide novel approaches to solving difficult issues. Classical optimization methods have problems with nonlinearity and numerous restrictions; bio-inspired algorithms, on the other hand, give better answers.