Nature inspired optimization algorithms or simply variations of metaheuristics?
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Nature inspired optimization algorithms or simply variations of metaheuristics? Alexandros Tzanetos1 · Georgios Dounias1
© Springer Nature B.V. 2020
Abstract In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing natureinspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new natureinspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms. Keywords Nature-inspired intelligent (NII) algorithms · Guidelines for nature-inspired algorithms · AI and optimization · Evaluation of algorithm’s innovation
1 Introduction Computational intelligence (CI) (Chen 1999) as an important and continuously emerging sub-field of artificial intelligence (AI) (Nilsson and Nilsson 1998) focuses on the setup of computational systems that can take decisions based on some rules or models and that are also able to generalize effectively from the analysis of available collections of data. The term Computational Intelligence includes different areas of related research like machine learning, evolutionary computation, hybrid and adaptive intelligence, etc. A newer “branch” of those Computational Intelligence approaches called nature-inspired algorithms * Alexandros Tzanetos [email protected] Georgios Dounias [email protected] 1
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, 41 Kountouriotou Str., 82132 Chios, Greece
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are becoming popular during the last decade, due to their ability of providing solutions of higher quality in difficult optimization tasks, in contrary to classical OR-approaches, such as mathematical programming. Most of nature inspired intelligent (NII) Algorithms are in fact intelligent meta-heuristic optimization methods. The main advantage of meta-heuristics compared to heuristic techniques is the ability to improve the population of candidate solutions based on intelligence collected during the algorithmic procedure. Specifically, Holland in the very beginning of evolutionary computation (Holland 1975) paved the way for NII, by introducing the first Genetic Algorithm in literature. Although Genetic Algorith
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