Repozytorium publikacji - Politechnika Gdańska

Artificial Intelligence (AI) is a scientific area that currently sees an enormous growth. Various new algorithms and methods are developed and many of them meets practical, successful applications. Authors of new algorithms draw different inspirations. Probably the most common one is the nature. For example, Artificial Neural Networks were inspired by the structure of human brain and nervous system while the classic Genetic Algorithm was inspired by the biological evolution process. One of the important areas of AI algorithms applications are optimization problems which can be encountered in practically all fields of science, technology, and everyday life. Amongst AI algorithms used to solve optimization problems, especially large, and still broadening group are swarm intelligence algorithms. They are nature-inspired, meta-heuristic algorithms which usually solve optimization problems by mimicking biological or physical phenomena. They are based mainly on observations of behaviours of various species of animals for example birds , ants , grasshoppers , bees , bats , wolves , fish , dolphins and many other or implement physics laws or environmental phenomena like laws of gravity , motion of galaxies , lightning formation , hydrologic cycle , water evaporation , etc. The general advantages of swarm optimization are: simplicity, easy implementation and the lack of the objective function gradient information requirement. They are usually fast converging and can bypass local optima. Despite large number of algorithms there is no one, ultimate algorithm that solves all types of problems (single- and multi-objective, uni- and multi-modal, with and without boundaries, etc.). Thus, there is a permanent need for more algorithms with new, original inspirations. The paper presents general advantages of swarm intelligence algorithms and a short review of selected, interesting optimization algorithms that draw inspirations from marine nature and cosmic space. These are Gravitational Search Algorithm, Artificial Fish Swarm Optimization, Krill Herd, Whale Optimization Algorithm and Salp Swarm Algorithm

Autorzy

• dr hab. inż. Marek Galewski link otwiera się w nowej karcie ,
• mgr inż. Piotr Duba link otwiera się w nowej karcie