Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique

Gupta, Neeraj; Patel, Nilesh; Tiwari, Bhupendra Nath; Khosravy, Mahdi

doi:10.1007/978-3-030-02686-8_55

Cited by 27 publications

(15 citation statements)

References 17 publications

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“…We have also tested with many variants of GA as given in Ref. [40], and deduce the observation based on the simulation results that, METO is a good replacement of GA for highly complex problems. It works best on single global solution problems with multiple local solutions.…”

Section: Observation and Discussion On 100 Variables Problemsmentioning

confidence: 99%

Mendelian evolutionary theory optimization algorithm

et al. 2020

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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 Epimuation, 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 (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) So

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Section: Observation and Discussion On 100 Variables Problemsmentioning

confidence: 99%

Mendelian evolutionary theory optimization algorithm

et al. 2020

Self Cite

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“…The main well-known technique is the genetic algorithm (GA). It reiterates the Darwinian principles of evolution [60,61]. Mendelian evolution has been also recently developed and suggested on multi-species as it is inspired from plant biology [62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Approach in Determining Critical Parameters for Optimum Safety Functions in Mega Projects (Case Study: Iran’s Construction Industry)

Heravi

Yeganeh

Razavian

2021

Springer Tracts in Nature-Inspired Computing

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“…To solve these problems, classical methods of mathematical optimization are widely used [17]. In recent years, new optimization methods have been developed, such as new crossover methods for real coded genetic algorithms that improve the quality of the solution, the data transfer rate and the determination of the optimal solution [18]; an optimization algorithm based on Mendel's evolutionary theory [19]; an evolutionary optimization method based on the biological evolution of plants [20]; a genetic algorithm based on the technique of extended selection and logarithmic mutations [21]; etc. Variants of genetic algorithms, particle swarm algorithms and Mendel's evolutionary theory are also presented in the book by Simon [22] and in the works of Chih-Ta and Ming-Feng [23], Haiping et al [24], Janet et al [25], Salman and Khan [26], Sidahmed [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

et al. 2021

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Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.

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Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique

Cited by 27 publications

References 17 publications

Mendelian evolutionary theory optimization algorithm

Mendelian evolutionary theory optimization algorithm

Using Fuzzy Approach in Determining Critical Parameters for Optimum Safety Functions in Mega Projects (Case Study: Iran’s Construction Industry)

Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

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