The term optimisation is not only mathematically important but also closely linked to any business. Everyone aims to optimise the way things are executed, processed, or delivered. For a given problem, there is always going to be the best way among the many possible solutions or approaches, and this is usually referred to as an optimised solution. Here are a few examples of business optimisation:
To solve any optimisation problem, there are three main fundamental elements that one needs to understand.
In the context of optimisation, a heuristic is defined as a method driven by rule-of-thumb without any specific procedure. A metaheuristics method is designed by leveraging more than one heuristic methods and thus inheriting all heuristic methods’ characteristics. Natures inspirations, such as ants searching for food, wolves hunting their prey, the formation of water droplets, diffusion of two chemicals, cell formation, the attraction between electrostatic objects, gravity, have resulted in the ‘Metaheuristics Algorithm’.
Metaheuristic algorithms’ adoption rate is surging in engineering, finance, retail, healthcare, insurance, and biomedical science. The optimisation is a critical factor in solving business and engineering problems. These algorithms can be summarised as a form of stochastic optimisation (an optimisation method that generates and uses random variables) independent of the surface gradient for optimisation. Most of these algorithms are inspired by different sources of nature and evolved from a purely mathematical model to a highly intelligent method of solving problems. Metaheuristics, along with machine language, is gaining wide acceptance in solving complex problems.