A Critical Analysis of 10+ Nature-Inspired Metaheuristic Algorithms for Real World Applications
Year: 2026
Type: conference-paper
Abstract: Metaheuristic algorithms (MAs) proved to be effective solution methods for complex optimization problems and drew increasing interest from industrialists and scientists. Recently, there is large the number of new MAs has grown substantially, and they have been drawn from human intuition, phenomena in natural processes, physical laws, or biological phenomena. Most of these algorithms have presented competitive results compared to conventional methods on widely used benchmark functions. Nonlinear and complex optimization problems, shared by the majority of scientific and engineering fields, are a great challenge to conventional algorithms because they are high-dimensional and have complicated search spaces. In a bid to bridge the gaps, researchers have come up with a broad array of natureinspired algorithms, notably the swarm-intelligence-based algorithms, that are adaptable, robust, and have effective global search techniques. In this work, we perform a detailed analysis of 10+ newly introduced bio-inspired swarm intelligence algorithms. Our classification of these algorithms and the main exploration-exploitation trade-off allows us to gain comprehensive insights into their design, performance, and ability to solve real-world applications.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203787355