Uniform distribution driven adaptive differential evolution
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Uniform distribution driven adaptive differential evolution Raunak Sengupta1 · Monalisa Pal2 · Sriparna Saha1 · Sanghamitra Bandyopadhyay2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Evolutionary algorithms are popular optimization tools for real-world applications due to their numerous advantages such as capability of parallel search along multiple directions by maintaining a population of candidates, invariance to certain mathematical properties (convexity, continuity and hardness) of fitness landscape and ability to handle black-box problems. However, most of the current evolutionary algorithms are loosely based on heuristics inspired by nature and lack the crucial theoretical background. Motivated by the overwhelming advantages of such optimization algorithms and the necessity for theoretical foundation, this paper presents a new evolutionary algorithm - UDE (Uniform Differential Evolution) for solving single- objective optimization problems along with a theoretical analysis of the proposed UDE algorithm. Thus, this paper formally gives insights about the features and properties of the various optimization strategies used. This method is different from traditional Differential Evolution variants as it employs a uniform probability distribution for generating new candidate solutions. UDE is further developed to obtain an adaptive evolutionary algorithm - Adaptive UDE (AUDE), which has shown to obtain significant improvements in the performance and convergence speeds compared to other algorithms on a benchmark set of 19 test problems. The source codes are available at http://worksupplements.droppages.com/ude aude. Keywords Box-constrained single objective optimization · Evolutionary optimization · Adaptive evolutionary algorithms · Reproduction operators · Differential Evolution
1 Introduction Evolutionary algorithms (EAs) focus on global optimization of functions [1, 2]. Current literature demonstrates that a large number of EAs like Differential Evolution (DE) [3, 4], Genetic Algorithms (GA) [5, 6], Particle Swarm optimization (PSO) [7, 8], Cuckoo Search (CVnew) [9], Ant Colony Optimization (ACO) [10, 11], Artificial Bee Colony (ABC) Optimization [12], Locust Search (LS) [13], Gray Wolf Optimization (GWO) [14], Social Spider Optimization (SSO) [15], etc., are used as the underlying optimization strategies in various real-world applications
This work is partially supported by the project (DST-INRIA/201502/BIDEE/0978) of Indo-French Centre for the Promotion of Advanced Research (CEFIPRA—IFCPAR), by J. C. Bose Fellowship (SB/SJ/JCB-033/2016) and “SERB Women Excellence Award 2018” (SB/WEA-08/2017) of Department of Science and Technology, Government of India. Monalisa Pal
monalisapal [email protected]
Extended author information available on the last page of the article.
such as feature selection [4, 16], clustering [8, 15], anomaly detection [17], forecasting [18], and many-more. Most of the EAs are inspired by the mechanisms of biological evolution and behaviors of living org
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