The emerging technology for translating the intention of human into control signals is the Brain–computer interface (BCI). The BCI helps the patients with complete motor dysfunction to interact with the people. In this research, a method for abnormality assessment in humans from the perspective of the BCI was proposed by developing a hybrid optimization algorithm based on Electroencephalography (EEG). The hybrid optimization algorithm, called Particle Rider Optimization Algorithm (PROA) is designed through the incorporation of Particle Swarm Optimization (PSO) and Rider Optimization algorithm (ROA). The pre-processing is done for filtering the noise and removal of artefact. In pre-processing, the noise is removed through the Common Average Referencing (CAR) and Laplacian filters, whereas the artifacts are eliminated by Principle component analysis (PCA).

In order to solve the deployment problem, which is considered a major issue that faces the design of efficient Wireless Sensor Networks (WSNs), a novel deployment algorithm based on an Enhanced Black Widow Optimization algorithm (EBWO) is proposed. The EBWO algorithm aims to determine the optimal number of sensors and their locations for optimizing both the coverage and the deployment cost. The BWO algorithm is adapted to solve the deployment problem by introducing a set of enhancements, which improves the search capability and the run time of the algorithm. A chaotic initialization is employed in the EBWO algorithm to strengthen the exploration capability of the initial population. Moreover, a modified reproduction mechanism is designed to assist the algorithm in optimizing the number of deployed sensors. Comparisons with modern state-of-the-art deployment methods show that the EBWO algorithm can deliver excellent solutions, where it is ranked first during all the simulations with a coverage difference varying between 3.34% and 7.94% from the other competitors.

Many high-demand industrial products are generated by microorganisms, including fuels, food, vitamins, and other chemicals. Metabolic engineering is the method of circumventing cellular control to manufacture a desirable product or to create a new product that the host cells do not normally need to produce. One of the objectives of microorganism metabolic engineering is to maximise the production of a desired product. However, owing to the structure of the regulatory cellular and metabolic network, identifying specific genes to be knocked out is difficult. The development of optimization algorithms often confronts issues such as easily trapping in local maxima and handling multivariate and multimodal functions inefficiently. To predict the gene knockout list that can generate high yields of desired product, a hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis (FADFBA) is proposed. This paper focuses on the ethanol production of Escherichia coli (E. coli). The findings of the experiments include gene lists, ethanol production, growth rate, and the performance of FADFBA.

In recent days, big data is a vital role in information knowledge analysis, predicting, and manipulating process. Moreover, big data is well-known for organized extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with the conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tools faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in the research area. In this paper, the Grey wolf Shuffled Shepherd Optimization Algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised to classify the big data. In this technique, for classifying the big data a hybrid classifier, termed as Holoentropy driven Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced. In addition, the developed hybrid classification model utilizes the MapReduce structure to solve big data issues. Here, the training process of the DRNN classifier is employed using GWSSOA. However, the developed GWSSOA is devised by integrating Shuffled Shepherd Optimization Algorithm (SSOA) and Grey Wolf Optimizer (GWO) algorithms. The developed GWSSOA-based DRNN model outperforms other big data classification techniques with regards to accuracy, specificity, and sensitivity of 0.966, 0.964, 0.870, and 209837ms.

This paper presents a genetic algorithm (GA) approach to solve the redundancy allocation problem (RAP) with imprecise parameters. The impreciseness has been considered here to tackle the unpredictable environment in real-life affairs which is directly striking the design parameters. Imprecise parameters are represented by several types of representations, viz. fuzzy, stochastic, and deterministic interval-valued. In the case of fuzzy and stochastic representations, the corresponding problems have been transformed into a deterministic interval optimization problem. Hence, to solve the problem, a GA based penalty function technique is proposed. The constraint(s) of the same are handled by constraints satisfaction rule which has been proposed in this paper. In the proposed approach, the constrained optimization problem has been converted into an unconstrained optimization problem by use of Big-M penalty technique. To solve the RAP with imprecise parameters, GA has been implemented. Finally, numerical experiments have been done for illustration purpose.

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

An advanced hybrid algorithm (haDEPSO) proposed in this paper for engineering design optimization problems. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE introduced a novel mutation, crossover and selection strategy, to avoiding premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also, in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Proposed hybrid haDEPSO as well as its integrating component aDE and aPSO has been applied to five engineering design optimization problems. Numerical, statistical and graphical experiments (best, worst, mean and standard deviation plus convergence analysis) confirm the superiority of the proposed algorithms over many state-of-the-art algorithms.

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

How cooperation emerges and persists in a population of selfish agents is a fundamental question in evolutionary game theory. The research shows that collective strategies with master-slave mechanism (CSMSM) defeat tit-for-tat and other well-known strategies in spatial iterated prisoner's dilemma. A CSMSM identifies kin members by means of a handshaking mechanism. If the opponent is identified as non-kin, a CSMSM will always defect. Once two CSMSMs meet, they play master and slave roles. A mater defects and a slave cooperates in order to maximize the master's payoff. CSMSM outperforms non-collective strategies in spatial IPD even if there is only a small cluster of CSMSMs in the population. The existence and performance of CSMSM in spatial iterated prisoner's dilemma suggests that cooperation first appears and persists in a group of collective agents.