Metaheuristics in Process Engineering: A Historical Perspective

Shelokar, Prakash; Kulkarni, Abhijit J.; Jayaraman, Vaidyanathan; Siarry, Patrick

doi:10.1007/978-3-319-06508-3_1

Cited by 2 publications

(4 citation statements)

References 241 publications

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“…Finally, based on Equations ( 7) to (9), it is possible to estimate the total data memory consumed by the entire GA. Given that n aux RAM is the amount of memory in bytes consumed by auxiliary variables, such as those used inside of the scope of loops, for instance, this amount can be expressed as…”

Section: Parameters Constraintsmentioning

confidence: 99%

“…Among the AI techniques, the genetic algorithms (GAs) are a kind of bioinspired metaheuristics used to solve search and optimization problems in several areas of industry and engineering 9 . The GAs are based on the Darwin's theory of evolution and they provide efficiency in solving numerous different kinds of problems 10 .…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7][8] Among the AI techniques, the genetic algorithms (GAs) are a kind of bioinspired metaheuristics used to solve search and optimization problems in several areas of industry and engineering. 9 The GAs are based on the Darwin's theory of evolution and they provide efficiency in solving numerous different kinds of problems. 10 However, they have a high computational complexity, which is affected by the population size and the evaluation function (also called fitness function).…”

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confidence: 99%

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Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Medeiros

Torquato

Fernandes

2020

Engineering Reports

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This work proposes a strategy to create an embedded genetic algorithms (GAs) for low-power, low-cost, and low-size-memory devices. This strategy aims to provide the means of GAs to run as a low-cost and low-power consumption embedded system, where microcontrollers (μCs) are commonly used. The implementation details are presented, emphasizing the limitations and restrictions imposed to turn it more compact and efficient. In addition, data related to the algorithm effectiveness, processing time, and memory consumption were obtained from simulations, oscilloscope measurements, and using the hardware-in-loop technique. Finally, this implementation is compared with other implementation from the literature and the results show that 8-bits μCs can run GAs for several practical applications.

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Section: Parameters Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Medeiros

Torquato

Fernandes

2020

Engineering Reports

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“…From the diagram, using the magnitude and location of the maximum driving force, the slopes of the operating lines are calculated which correspond to the minimum reflux (𝑅𝑅 𝑚𝑖𝑛 ) and reboiler ratio (𝑅𝐵 𝑚𝑖𝑛 ). Actual 𝑅𝑅 and 𝑅𝐵 values are obtained after multiplying the 𝑅𝑅 𝑚𝑖𝑛 and 𝑅𝐵 𝑚𝑖𝑛 values by 1.2 as a rule of thumb (Shelokar et al, 2014). These are the optimized values of the two degrees of freedoms required for the steady state simulation.…”

Section: 1: Rd Column Design Parameter Determination Through the Driv...mentioning

confidence: 99%