Table 1.

A brief description of the different optimization techniques

Optimization technique	Introduction	Importance	Example	References
Randomized blocks	It is the common technique that will be used to eliminate any unnecessary factors known as the nuisance factor that will arise during the experimental procedure	This technique improves the accuracy of comparisons by eliminating any variability that may arise	Lipolytic enzyme and lignin	Kumar and Knowles (1993); Carreiro et al. (2017)
Taguchi method	This method uses the process of identification of proper control parameters that will provide the most optimum result	Results of these experiments are used to analyze the data and predict the quality of components produced	Cellulase, protease, xylitol, laccase, etc.	Athreya and Venkatesh (2012); Biswas et al. (2014); Sinha and Singh (2016)
Factorial designs	Experiments are generally conducted to study multiple factors, so the most commonly used technique for this purpose shall be the factorial design technique and it has been found to be the most efficient in solving experiments where multiple factors influence the process	The estimations that are done using this technique give results which are valid over a wide range of conditions that are applicable in the experiments	Lipase, amylase, hydrolytic enzyme, tannase, etc.	Negi and Banerjee (2006); Dange and Peshwe (2015)
Response surface methodology (RSM)	It is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response	It is a sequential procedure and the main objective is to lead to the final result both rapidly and efficiently. The RSM determines the final optimum operating condition of the system or the region in which the set operating parameters are fulfilled	Lingo-cellulolytic, bio-diesel, alkaline proteases, lactase, etc.	Bruns et al. (2014); Kunamneni et al. (2015); Kunamneni and Singh (2016)
Central composite design (CCD)	It is the technique used to build the second-order model for response variable as an alternative to the factorial type of representation	The total number of experiments that have to be run is calculated and the required matrix is defined, then using the linear regression methodology the final result is recorded from the experimentation	Ethanol, phytase, xylanase, chitinase, etc.	Mohana et al. (2008); Karmakar and Ray (2011); Khaleel et al. (2012)
Artificial neural network (ANN)	Developed using the biological neuron as the reference material for developing intelligent systems which can be used as an advancement to the current scale of study	A large number of simple processors with many interconnections. ANN models attempt to use some “organizational” principles believed to be used in the human	Amyloglucosidase, protease, lipase, glucoamylase	Chang et al. (2006); Desai et al. (2008); Ying et al. (2009); Funes et al. (2015)