In today's world of engineering evolution, the need for optimized design has led to development of a plethora of optimization algorithms. Right from hardware engineering design problems that need optimization of design parameters to software applications that require reduction of data sets, optimization algorithms play a vital role. These algorithms are either based on statistical measures or on heuristics. Traditional optimization algorithms use statistical methods in which the optimal solution may not be the global minimal point. These standard optimization techniques are more application specific and demand different parameter sets for different applications. Rather, the bio‐inspired meta‐heuristic algorithms act like black boxes enabling multiple applications with definite global optimal solutions. This review work gives an insight of various bio‐inspired optimization algorithms including dragonfly algorithm, the whale optimization algorithm, gray wolf optimizer, moth‐flame optimization algorithm, cuckoo optimization algorithm, artificial bee colony algorithm, ant colony optimization, grasshopper optimization algorithm, binary bat algorithm, salp algorithm, and the ant lion optimizer. The biological behaviors of the living things that lead to modeling of these algorithms have been discussed in detail. The parametric setting of each algorithm has been studied and their evaluation with benchmark test functions has been reviewed. Also their application to real‐world engineering design problems has been discussed. Based on these characteristics, the possibility to extend these algorithms to data set optimization, feature set reduction, or optimization has been discussed. This article is categorized under: Algorithms and Computational Methods > Algorithms Algorithms and Computational Methods > Computational Complexity Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing

Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a twoclass classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded