Comparative Analysis of Nature-Inspired Optimization Algorithms: Applications, Challenges and Future Directions

With the proliferation of data generation, the process of achieving optimal solutions is getting more complex. It is becoming increasingly clear that intelligent metaheuristics algorithms are the way to go for solving these complicated optimisation problems, particularly when faced with several restrictions. The development of effective methods for dealing with these optimisation challenges has prompted the creation of numerous new algorithms. These algorithms are either improving their ability to handle problems in many domains or are investigating new contexts in which they could be useful. The field is advancing at a quick pace, leaving many in the dark about its potential uses in other fields. In order to fill this knowledge vacuum, this paper discusses the development, principles, and domain of application of prominent optimisation algorithms that are inspired by nature. The following algorithms have been studied and examined: spider monkey, cat swarm, moth-flame and crow search algorithm. Additionally, this evaluation can be used as a roadmap for selecting suitable algorithms for upcoming research. Because there is a dearth of substantial literature, we have discovered a number of algorithms that draw inspiration from nature and possess enormous theoretical and practical potential.