## 2 Related Work
(2015), designed a variation of the binary firefly algorithm called return-cost-based FFA (Rc-FFA), which was able to prevent premature convergence. A binary dragonfly optimizer was developed by [PERSON] et al. (2016), which employed a time-varying transfer function that improved its exploitation and exploration phases. However, its performance was not close to optimal. [PERSON] et al. (2017), proposed two variants of the salp swarm algorithm (SSA) to solve the feature selection problem. The first utilized eight transfer functions to convert a continuous search space to a binary one, and the other introduced a crossover operator to improve the exploration behavior of the SSA; however, the study did not provide an analysis of the transfer functions. A binary grasshopper optimization algorithm (BGOA) was proposed by [PERSON] et al. (2018), using the V-shaped transfer function and sigmoid. This study incorporated the mutation operator to enhance the exploration phase of the BGOA. In [PERSON] and [PERSON] (2019), two binary versions of the whale optimization algorithm were proposed. The first utilized the effect of a roulette wheel and tournament mechanisms of selection with a random operator in the process of searching, while the second version employed the mutation and crossover mechanisms to enhance diversification. [PERSON] et al. (2019), proposed a binary seagull optimizer which employed four S- and V-shaped transfer functions to binarize the baseline algorithm, applying it to solve the feature selection problem. The reported results showed competitive performance with other methods; their technique was also evaluated using high-dimensional datasets. [PERSON] et al. (2018), and [PERSON] et al. (2019), designed bio-inspired metaheuristics comprising three algorithms. The former combined glowworld swarm optimization, lion optimization algorithm and differential evolution, while the latter hybridized krill herd, cat swarm and bacteria foraging optimizers, with both using the AddaBoostSVM classifier as the fitness function and a backpropagation neural network to perform classification, which was applied to clinical diagnoses of diseases. The methods showed superior performance over other methods. However, these proposed methods were computationally expensive due to the use of combinations of different metaheuristic methods. [PERSON] and [PERSON] (2019), proposed a bio-inspired method (salp swarm) with kernel-ELM as a classifier to diagnose glaucoma disease from medical images. The results produced by this method showed superior performance over other methods. However, the technique was not tested on collections of large, real-time datasets because this proved to be more challenging. The different algorithms mentioned above provided better solutions to many of the feature selection problems (2019). Many of these methods, however, could not yield an optimal subset of features for datasets of high-dimensional magnitude. Additionally, the inference from the NFL theorem that no single algorithm can solve all optimization problems holds in the feature selection domain as well. Hence, a new binary method needs to be developed to solve the optimization problem of feature selection. Some bio-inspired metaheuristic algorithms are based on susceptible infectious recovery (SIR), the class of models to which the EOSA algorithm belongs. Therefore, reviewingsome efforts made using this model in the literature is appropriate here. Some such methods have been proposed to tackle the problem of detection and classification, among which we may cite the SIR model [35]. This approach is based on sample paths and was employed to detect the sources of information in a network. The assumption of that study was that all nodes on the network were in their initial state and were susceptible, apart from a source that was in a state of infection. The susceptible nodes could then become infected by the infected node, which itself may have no longer been infected.