An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions

• Published in: Cognitive computation Vol. 10; no. 4; pp. 517 - 544
• Main Authors: Molina, Daniel, LaTorre, Antonio, Herrera, Francisco
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Benchmarks; Biomedical and Life Sciences; Biomedicine; Biomimetics; Competition; Computation by Abstract Devices; Computational Biology/Bioinformatics; Continuous improvement; Covariance matrix; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Global optimization; Neurosciences; Optimization; Proposals; Global optimization; Large-scale global optimization; Multimodal optimization; Real-parameter competitions; Continuous optimization
• ISSN: 1866-9956; 1866-9964
• DOI: 10.1007/s12559-018-9554-0

Over the recent years, continuous optimization has significantly evolved to become the mature research field it is nowadays. Through this process, evolutionary algorithms had an important role, as they are able to obtain good results with limited resources. Among them, bio-inspired algorithms, which mimic cooperative and competitive behaviors observed in animals, are a very active field, with more proposals every year. This increment in the number of optimization algorithms is apparent in the many competitions held at corresponding special sessions in the last 10 years. In these competitions, several algorithms or ideas have become points of reference, and used as starting points for more advanced algorithms in following competitions. In this paper, we have obtained, for different real-parameter competitions, their benchmarks, participants, and winners (from the competitions’ website) and we review the most relevant algorithms and techniques, presenting the trajectory they have followed over the last years and how some of these works have deeply influenced the top performing algorithms of today. The aim is to be both a useful reference for researchers new to this interesting research topic and a useful guide for current researchers in the field. We have observed that there are several algorithms, like the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE), Mean-Variance Mapping Optimization (MVMO), and Multiple Offspring Sampling (MOS), which have obtained a strong influence over other algorithms. We have also suggested several techniques that are being widely adopted among the winning proposals, and that could be used for more competitive algorithms. Global optimization is a mature research field in continuous improvement, and the history of competitions provides useful information about the past that can help us to learn how to go forward in the future.