Mendelian evolutionary theory optimization algorithm
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Mendelian evolutionary theory optimization algorithm Neeraj Gupta1 · Mahdi Khosravy2
· Nilesh Patel1 · Nilanjan Dey3 · Om Prakash Mahela4
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This study presented a new multi-species binary coded algorithm, Mendelian evolutionary theory optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: first, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second, the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimutation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators—(1) Flipper, (2) Pollination, (3) Breeding, and (4) Epimutation—are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers: (1) binary hybrid GA, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes. Keywords Mendelian evolutionary theory · Rehabilitation · Binary coded optimizer · Pollination · Meta-heuristic optimization · Multi-species · Artificial DNA
1 Introduction Optimization plays an essential role in achieving accuracy and increasing efficiency of systems. Under the classes of real and binary coded schemes, the literature proposes a variety of meta-heuristic population-based evolutionary algorithms (EAs) (Khosravy et al. 2020a; BoussaïD et al. 2013; Dey et al. 2020), i.e., genetic Algorithm (GA) (Liang and Wang 2019; Communicated by A. Di Nola.
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Mahdi Khosravy [email protected]
1
School of Engineering and Computer Science, Oakland University, Rochester, MI, USA
2
Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University, Osaka, Japan
3
Techno International New Town, Kolkata, India
4
Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India
Gupta et al. 2020b), memetic algorithm (MA) (Tang et al. 2019), PSO (AlRashidi and El-Hawary 2008), etc. Although the population-
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