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IJATCA solicits original research papers for the **January â€“ 2022** Edition.

Last date of manuscript submission is **January 30, 2022**.

Volume: 8 Issue: 3

Year of Publication: 2021

Authors: Charvee M Gurav, Bhargavi A Ketkar, Sahil P Patil, Prashant A Giri,

Damini D Apankar, Sonam S Gawas

Year of Publication: 2021

Authors: Charvee M Gurav, Bhargavi A Ketkar, Sahil P Patil, Prashant A Giri,

Damini D Apankar, Sonam S Gawas

Differential Evolution (DE) is an evolutionary optimization technique that is very simple, fast, and robust at numerical optimization. It has mainly three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. The main advantage of the DE over other methods is its stability. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. DE becomes impressive because of the parameters; crossover ratio (CR) and mutation factor (F) do not require the same tuning which is necessary in many other Evolutionary Algorithms. In the present study, DE has been used to solve the two chemical engineering problems from the literature. The comparison is made with some other well-known conventional and non-conventional optimization methods. From the results, it was observed that the convergence speed of DE is significantly better than the other techniques. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.

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Optimization, Differential Evolution, Genetic Algorithms, Evolutionary Algorithms.

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