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.
Highlights
Optimization plays an essential role in achieving accuracy and increasing efficiency of systems
We found that for few functions such as Ackley, Rosenbrock Leon, Dixon price, CF-3, HCF-1 optimizer Covariance Matrix Adaptation Evolutionary Strategy (CMAES), Gravitational Search Algorithm (GSA), SLPSO, Biogeography-Based Optimization (BBO), and Teaching Learning Based Optimization (TLBO) give the better solution than METO
This paper presents a novel bio-inspired meta-heuristic binary coded optimization technique
Summary
Optimization plays an essential role in achieving accuracy and increasing efficiency of systems. To add the state-ofthe-art, we inspired from the evolution theory of plant genetics based on Mendel’s inheritance law to propose a genetically evolved optimization algorithm. In this algorithm, the evolution process takes place by interbreeding the plants of different species [32].To design a novel Evolutionary algorithm (EA), we redefine the biologically inspired metaphors in the binary domain to implement as computer program. (vii) Because recessive genes are transmitting in alternative generations (F0 to F2, without appearing in F1), subjected to the mutation multiple times over an evolution cycle It resembles the rehabilitation process of nature in self organizing. The conclusion is derived based on comparative results and statistical analysis of the METO with other optimizers
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