Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline that makes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with symbols instead of numeric values making a good base for symbolic reasoning. These symbols should be carefully designed as they form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined by membership, nonmembership, and indeterminacy degrees in neutrosophic system. This research proposes a MANETs attack inference by a hybrid framework of Self-Organized Features Maps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in knowledge discovery. The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is more accurate than other IDS found in literature.

This article suggests a novel enhanced slime mould optimizer (ESMO) that incorporates a chaotic strategy and an elitist group for handling various mathematical optimization benchmark functions and engineering problems. In the newly suggested solver, a chaotic strategy was integrated into the movement updating rule of the basic SMO, whereas the exploitation mechanism was enhanced via searching around an elitist group instead of only the global best dependence. To handle the mathematical optimization problems, 13 benchmark functions were utilized. To handle the engineering optimization problems, the optimal power flow (OPF) was handled first, where three studied cases were considered. The suggested scheme was scrutinized on a typical IEEE test grid, and the simulation results were compared with the results given in the former publications and found to be competitive in terms of the quality of the solution. The suggested ESMO outperformed the basic SMO in terms of the convergence rate, standard deviation, and solution merit. Furthermore, a test was executed to authenticate the statistical efficacy of the suggested ESMO-inspired scheme. The suggested ESMO provided a robust and straightforward solution for the OPF problem under diverse goal functions. Furthermore, the combined heat and electrical power dispatch problem was handled by considering a large-scale test case of 84 diverse units. Similar findings were drawn, where the suggested ESMO showed high superiority compared with the basic SMO and other recent techniques in minimizing the total production costs of heat and electrical energies.

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Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.

The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

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This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

This paper is concerned with the application of hybrid fuzzy-JAYA optimization algorithm to find the solution of non-linear optimal reactive power dispatch (ORPD) problem in power systems. The proposed hybrid optimization algorithm combines the merits of fuzzy principle and the Jaya optimizer. Fuzzification of the ORPD variables is employed by pseudo goal strategy. Two technical objectives are minimized individually and simultaneously to enhance the overall power systems performance. These objectives are transmission active power losses and voltage deviation at load buses. The ORPD objectives are optimized considering both inequality and equality constraints that reflect the operation needs. The hybrid fuzzy-JAYA is established as efficient optimization method that is achieving the global optimal solution. The effectiveness of the proposed hybrid algorithm for solving the ORPD problem is proven by using three standard IEEE test networks. An assessment of the proposed hybrid algorithm is carried out compared with other optimization algorithms those reported in the literature. The simulation results assure that the fuzzy Jaya hybrid algorithm leads to significant power system performance enhancement for different scale power systems. INDEX TERMS Fuzzy logic, jaya optimization algorithm, hybrid strategy, ORPD, pseudo goal strategy, minimization of transmission power losses, enhancement of load buses voltage profile.