## 1 Introduction  
Advances in computer technologies and biometrics are having a huge impact on security applications for society [1]. Human identification using biometric technologies has attracted more attention in security applications. Some examples of security applications are access control [2], border control, [3], banking [4], and forensics [5]. Human recognition research projects promise new life to many security-consulting firms and personal identification system manufacturers. There are various biometric technologies such as iris [6], face [7], fingerprint [8], palm print [9], hand geometry [10], voice [11] and veins [12]. Among these technologies, iris recognition has received more attention due to its inconvenient usage as security for various systems [13]. Following the fundamental principle that interclass variation should be larger than intraclass variation, iris patterns offer a powerful alternative approach to reliable visual recognition of persons when imaging can be done at distances of about a meter or less, and especially when there is a need to search very large databases without incurring any false matches despite a huge number of possibilities. Although it is small with an 11 mm diameter and sometimes problematic to image, the iris has the great mathematical advantage that its pattern variability among different persons is enormous. In addition, as an internal organ of the eye, the iris is well protected from the environment and stable over time. Iris is robust to ageing factors compared with other biometrics authentication parts of the human. As a planar object, its image is relatively insensitive to the angle of illumination, and changes in viewing angle cause only affine transformations; even the nonaffine pattern distortion caused by pupillary dilation is readily reversible in the image coding stage [14]. Iris is regarded as one of the most trustworthy biometric traits for human identification for a variety of reasons, including the fact that it is a highly protected internal organ that is visible from the outside, and that iris patterns are highly distinctive with a high degree of freedom, that iris patterns are largely stable over time, etc. Modern iris recognition algorithms have shown encouraging results for iris recognition. Binary characteristics are used by the majority of iris recognition algorithms (i.e., iris codes). Iris recognition systems may be deployed widely because of the enormous advantages in memory and processing cost provided by the binary character of iris codes. With millions of individuals registered, the current countrywide iris recognition system deployments in the United Arab Emirates (UAE) [15] and India [16] are regarded as successful. By quantifying the probabilistic information that random variables might potentially send to one another, information theory [17] examines correlations between them. Its techniques are appropriate for inference, communication channels, classifiers, and pattern recognition in general, but have not seen much use in the realm of biometrics outside of cryptographic protocols. This is strange because several ideas in biometrics, like the capacity of a noisy channel, an encoding scheme's efficiency, and the entropy of a random variable or code, strongly resemble these concepts. For instance, it is generally understood that the randomness and complexity of biometric patterns determine their individuality and, consequently, their capacity to avoid collisions with other individuals (False Matches), but these biometric properties are infrequently quantified in terms of entropy. It's time to develop a quantitative and comprehensive understanding of the genesis of biometric collision avoidance, especially now that roughly a billion people have had their iris codes registered in a national ID deployment across India for de-duplication checks [18]. Meta-heuristic searching optimization is a random searching algorithm with the capability of embedding heuristic knowledge for finding the best solution from a huge number of candidate solutions.