ABSTRACT
Bio-inspired meta heuristics are stochastic algorithms designed to solve advanced optimisation problems. They are characterised by their ease of implementation, simple structure, and ability to avoid local minima. These algorithms often achieve significantly better results than classical optimisation methods, such as sequential quadratic programming, quasi-Newton methods, conjugate gradient methods, and fast steepest descent [125]. It should be emphasised that bio-inspired metaheuristics do not guarantee optimal solutions due to their stochastic nature, but they provide satisfactory results within a reasonable timeframe. Given that most real-world problems are NP-hard, these algorithms are widely used to tackle such challenges [2]. There are several groups of biology-inspired algorithms [5]:
Evolutionary algorithms — based on Darwin's theory of natural selection.
Swarm algorithms — based on the concept of swarm intelligence [19].
Human-based algorithms — modelling various human activities.
Physics-based algorithms — grounded in the laws of physics and chemistry.
