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The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

The low system inertia and the high sensitivity to load and generation fluctuations represent the main challenges for future ambitious plans of modern power systems accompanied by high penetrations levels of the renewable energy sources (RESs). Therefore, this article presents a new approach for solving the load frequency control (LFC) in addition to the virtual inertia control (VIC) in interconnected RESs penetrated power systems using cooperative tilt-based controllers and a hybrid modified particle swarm optimization with genetic algorithm (MPSOGA). The VIC system is adopted using superconducting magnetic energy storage (SMES) to provide sufficient inertial energy for system stability. Two tilt-based controllers are employed in each area using the tilt-integral-derivative (TID) controller for the SMES and TID with filter (TIDF) for the LFC function. The cooperative optimum design of the TID/TIDF controllers leads to the enhancement of frequency stability in studied two-area power systems. The formulated optimization process aims to minimize the frequency nadir settling time during abrupt changes of RESs and/or load changes, considering the cooperative control of LFC and VIC. The proposed approach has been applied to a case study consisting of two-area power systems, connected via hybrid high voltage DC/AC (hybrid HVAC/HVDC) tie-line, integrated with distributed conventional generations, photovoltaic (PV), and wind generation systems. Performance analysis has been conducted to demonstrate the effectiveness of the proposed method is compared to the genetic algorithm (GA) and particle-swarm optimization (PSO) using high fluctuations of renewable generations under extreme changes in loading conditions and physical parameters variation. The obtained results show the superiority of MPSOGA approach on the other competitive optimization techniques.

The wind energy characteristics at the site not only indicate the amount of energy obtained from wind energy, but it also depends on the control strategy implemented on the wind energy conversion system. This study proposes an application of Ant Lion Optimizer (ALO) for tuning the classical (PI) controller parameters for the permanent magnet synchronous generator driven by wind energy system, to such an extent that the most maximum power point tracking can be acknowledged along with an improved fault ride-through capability. The optimized PI-controller parameters based ALO improving the low voltage ride through (LVRT) performance and the maximum power point tracking. ALO is implemented to the classical PI-controller on the machine side converter system, and grid side converter, for maximum power extraction from PMSG and enhancing LVRT. Four cases were considered in this paper, to examine the feasibility of the proposed ALO-PI controller. The suggested cases are system running at normal condition, a step-change in wind speed, wind speed variation at normal grid condition, and three-phase fault. A significant improvement in the system dynamic performance when applying the ALO-PI controller is compared with classical PI-controller.

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.

Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

This paper proposes a new method for diagnosis Broken Rotor Bar (BRB) faults in three phase squirrel-cage induction motors. The proposed method is based on the stator current signature analysis usi...

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

This paper proposes a new method using Artificial Neural Network (ANN) for detection of different Inter Turn Short Circuit (ITSC) faults in an induction motor under different loading conditions. The stator current signal was obtained experimentally from a healthy motor and a faulty motor with ITSC faults. The statistical time domain features was extracted from stator current signal, these features are used to train and test an ANN in order to diagnose ITSC faults. A complete study is performed by considering various diagnosis methods from ANN and machine learning algorithms, including Decision Tree (DT), K-Nearest Neighbours (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) for diagnosis ITSC faults. The performance of the proposed method was compared with machine learning algorithms, the proposed method has a higher accuracy than the other algorithms. Trained neural networks are able to classify different states of the ITSC faults with satisfied accuracy. The efficiency of this approach has been proven using experimental tests to diagnose ITCS faults in a 1.5Hp squirrel cage induction motor.

This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.

Renewable energy resources are promising solution to meet the increasing energy demand and corresponding conventional resources depletion. Hybrid Micro-Grid System (HMGS) can utilize all energy resources in a certain location. In this paper, a HMGS consists of PV, wind, and battery integrated with diesel generator is considered. The problem of optimal component sizing is formulated in multiobjective optimization framework considering three objective functions, cost of Electricity (COE), Loss of Power Supply Probability (LPSP), and Renewable Factor (RF). Two planning scenarios, namely PV/wind/battery and PV/wind/battery/diesel, are modeled and used. In the study, the hourly meteorological data for SHLATEEN city in EGYPT is used. The proposed energy management strategy is applied during supplying the load. Then, the results of different three multi-objective optimization algorithms are compared to obtain the most reliable and cost-effective system configuration.

This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Levy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms.

This paper presents, Ant Lion Optimizer (ALO), Flower Pollination algorithm (FPA), Whale Optimization Algorithm (WOA), Satin Bowerbird Algorithm (SBO) and Moth Swarm Optimization(MSA) for determining the optimal location and sizing of DGs to minimize power losses in radial distribution networks(RDN) and improve the voltage profile. DGs operating at different power factors are considered that is (unity and optimal). The maintenance, operation and investment costs are formulated to form the total costs of DGs and also all the profits that DG Owner‟s (DGOs) are considered at the same time. The proposed methods are implemented on IEEE 33, 69 and 119-bus test systems and the obtained results of the proposed methods are compared with other popular methods available in the literature for validation purpose.

In this document, the topic of discussion is the combination of two existing algorithms to generate a new hybrid technique. The two algorithms that are subjected to said amalgamation are Genetic Algorithms (GA) and Stain Bowerbird Optimization algorithms (SBO). These two methodologies have profound utility themselves and are used in a multitude of scenarios. The easy application and the constructive outcomes manifested by these two algorithms birthed the idea of their combined usage. Following up on this, the hybrid GASBO was created. GASBO was an optimization approach used to detect and categorize the allotted renewable energy assets in a specific energy generation complex. This was done to regulate the energy dispensing systems otherwise known as ‘distributing’ systems. These renewable resources are reflected by environmental factors and the energy they create is also dependent on their surroundings. Factors like sunlight, rain, waves, and tides etcetera play major roles in determining the outcome of the created energy. Contrary to what it may appear like, the position of the DG sources in the structure affects the outcome a lot. These sources contain fuel cells and photovoltaic cells: in short, devices that can harness energy from a seemingly infinite supply like sunlight. As mentioned before, the GASBO assisted in providing the best location for the system and it also categorized the sources according to their abilities. The potential and position of the sources in the grid are of vast importance. The main purpose of GASBO is to optimize the overall system by improving its efficiency and reducing collateral harm. This shows that GASBO is quite a fundamental tool. It has also been tested on several systems like IEEE 33-bus. The facts in this paper are based on published projects.

This work presents a novel Moth Swarm Algorithm (MSA), inspired by the orientation of moths towards moonlight to solve constrained Optimal Power Flow (OPF) problem. The associative learning mechanism with immediate memory and population diversity crossover for Levy-mutation have been proposed to improve exploitation and exploration ability, respectively, in addition to adaptive Gaussian walks and spiral motion. The MSA and four heuristic search algorithms are carried out on the IEEE 30-bus, 57-bus and IEEE 118-bus power systems. These approaches are applied to optimize the control variables such as real power generations, load tap changer ratios, bus voltages and shunt capacitance values under several power system constraints. Fourteen different cases are executed on different curves of fuel cost (e.g., quadratic, valve-loading effects, multi-fuels options), environmental pollution emission, active power loss, voltage profile and voltage stability for contingency and normal conditions, in single and multi objective optimization space. Furthermore, the impacts of the updating mechanism of optimizers on those objective functions are investigated. The effectiveness and superiority of the MSA have been demonstrated in comparison with many recently published OPF solution

This paper presents a novel controller design of a thyristor controlled series capacitor based on an optimized adaptive neuro-fuzzy inference system. The modified states of matter search algorithm are implemented to fit the premise and consequent parameters of the neuro fuzzy system. The design objectives are to reduce the power system oscillations and find a minimum number of strongest fuzzy rules, trends toward building a low-size controller model. Therefore, fuzzy decision-making mechanism is employed to rank the Pareto-optimal set to extract the best compromise solution. The proposed controller design depends upon the expected wide range of operating conditions. The effectiveness of smart control strategy based controller is tested and compared on single machine infinite bus and multimachine power systems under small scale disturbance as well as large scale disturbances.

This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features’ set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.

In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions, and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

This study focuses on developing a multi-objective framework to seek out the optimal Distributed Generations (DGs) placement and sizing in large scale distribution networks. Renewable energy resources like wind turbine (WT), photovoltaic (PV) are employed as resources of Distributed Generation (DG). The well known and Non-dominated Sorting Genetic (NSGA-III) Algorithm is implemented to handle various objective functions such as active power losses, voltage deviation and voltage stability index. The proposed method is tested on standard IEEE 118-bus radial distribution networks. The proposed algorithm is used a range of non-dominant Pareto-optimal solutions that are stored in the external archive and then the ‘best’ compromise solution is identified by fuzzy sets technique. The simulation results show that the proposed methodology ready to provide well distributed Pareto optimum solutions for the multi-objective optimal power flow problem. Furthermore, in order to validate the obtained results Multi-objective Dragonfly (MODA) algorithm is performed also and the simulation results of two algorithms are compared with each other

This paper aims to propose Satin Bowerbird Optimization (SBO) Algorithm and Ant Lion optimizer (ALO) Algorithm for optimal sitting and sizing of renewable distributed generations (RDGs) (such as Photovoltaic (PV) and wind turbine (WT)) in radial distribution networks (RDN) to reducing the system power loss and consequently maximizing the net saving. The proposed methods are implemented on 33-bus and 118-bus IEEE standard radial distribution test systems. To ensure the validation of the proposed approaches, the simulation results obtained are compared by the proposed methods with each other and with the other existing technology.

In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i.e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.

The direct drive permanent magnet synchronous generator (PMSG) have many advantages consequently, it ends up a standout amongst the most appealing variable speed wind generation types. This paper proposes a novel application of a Whale Optimization Algorithm (WOA) aims at finding the optimal parameters of the conventional Proportional-Integral (PI) controllers for the permanent magnet synchronous generator based wind energy conversion system (WECS). The Whale Optimization Algorithm (WOA) is applied to the classical PI-controller on machine side converter system to find the optimal gains of the PMSG, to such an extent that a maximum power point tracking (MPPT) under normal condition, a step change of wind speed and the optimal dynamic performance of PMSG under grid fault can be easily achieved. Three case studies in this paper, dynamic responses of the system under normal operation, a step change of wind and a three-phase symmetrical fault with using crowbar protection and without crowbar protection. The optimal values that result from the WOA algorithm are compared with the simplex method.

In this paper, the performance of different optimization techniques namely, Moth Swarm Algorithm (MSA) and Whale Optimization Algorithm (WOA) are presented and compared for minimization of single and multi-objective namely, (total network power losses, and voltage deviation) in radial distribution systems by identify the Optimal Placement of Distributed Energy Resources (DER) together with capacitor (C). In this regard, multiple-DER units are analyzed under two load power factors (i.e., unity and optimal) with and without C. The proposed algorithms have been tested on the 33-bus radial distribution network. The performance of the different optimization algorithms has been compared with other evolutionary optimization technique under different system operating conditions. The simulation results observed the impact of integrating the proper size of DER at the suitable placement based on different techniques with and without C.

A number of different optimization algorithms namely, Moth Swarm Algorithm (MSA), and Particle Swarm Optimization (PSO) algorithm are presented and compared in this paper. Different optimization techniques are used to determine the optimum allocation of distributed generation (DG) in radial distribution systems during reduction of single and multi-objective function namely, (total network power losses, voltage deviation, and total operating cost). The multi objective function is formed by using the weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i.e., unity and optimal). The proposed algorithms have been tested on the 33-bus radial distribution network. The performance of the different optimization algorithms has been compared with other evolutionary optimization technique under different system operating conditions. The simulation results observed the impact of integrating the proper size of DG at the suitable placement based on different techniques.

WhatsApp is a widely used social media app, growing in popularity across the Middle East, and the most popular in Saudi Arabia. In this paper, we investigate the usage of WhatsApp as an educational support tool in a Saudi university. An online survey was constructed to ascertain how students and staff feel about and utilize WhatsApp as part of their daily studies. It also aimed to gather their thoughts on other platforms offered by the university such as Blackboard and email. The survey was tested and the results analyzed for frequency distributions, mean score, and standard deviation. Our results from nearly 200 student and staff members reveals that WhatsApp is heavily utilized for a variety of educational support tasks and greatly preferred over the other platforms. We propose that WhatsApp has good potential to support not only student coordination, information dissemination and simple enquiries but also to support formal teaching and out-of-class learning.

This paper aims to present and compare a number of optimization algorithms techniques namely, Whale Optimization Algorithm(WOA), Dragonfly Algorithm (DA) and Moth-Flame Optimization (MFO) algorithm. These approaches have been implemented to identify the optimal location and sizing of distributed generation (DG) in radial distribution systems considering minimization of network power losses. In this regard, multiple-DG units are simultaneously allocated and analyzed under two load power factors (i.e., unity and optimal). The proposed algorithms have been tested on the 69- and 119-bus radial distribution systems. The performance of the competitive algorithms has been compared with other evolutionary optimization methods under different system operating conditions in terms of all measures. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

Grey wolf optimizer (GWO) is a new technique, which can be applied successfully for solving optimized problems. The GWO indeed simulates the leadership hierarchy and hunting mechanism of grey wolves. There are four types of grey wolves which are alpha, beta, delta and omega. Those four types can be used for simulating the leadership hierarchy. In order to complete the process of GWO a three main steps of hunting, searching for prey, encircling prey and attacking prey are implemented. This work describes a novel meta-heuristic based on grey wolf optimization for optimum allocation of STATCOM devices on power system grid to minimized load buses voltage deviations and system power losses. Bus voltages have been solved by controlling the reactive power of shunt compensator. The Contingency management problem (such as system over-loading and a single line outages) by optimum installation of STATCOM devices, has been presented. Simulations are performed on IEEE 30-bus power system indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using traditional algorithms.

This paper presents a new Single/Multi-Objective Optimization algorithm inspired by hunting behavior of Grey Wolves (S/MOGWO). Modification is introduced to enhance the convergence rate of GWO. The proposed algorithm has been applied to minimize the emission grades of deleterious pollutants and to reduce the generating cost individually. Also, the proposed MOGWO is used to generate Pareto-optimal solutions for simultaneous minimization of the environmental pollution emissions along with the economic cost. Furthermore, fuzzy decision approach process is implemented to rank and extract the global Pareto-optimal solutions as the most suitable non-dominated solution. The effectiveness of the proposed methodology is tested on IEEE 30-bus system. The generation and security constraints are incorporated into the objective function to achieve a valid and accurate solution. The comparative study with different other techniques confirms the primacy of the proposed algorithm and its potential to solve the OPF problem in single and multi-objective optimization space.

This paper introduce a novel design of the static VAR compensator (SVC) controller for damping power system oscillations. A multi layer neural network model tuned by Grey Wolf Optimization algorithm (GWO) is investigated and presented. GWO search algorithm is used to optimized all the connection of weights and biases for the artificial neural network. The proposed approach depends up on the expected wide range of the effective operating conditions of the SVC. Modification is introduced in the proposed optimizer exploration-exploitation balance to enhance its rate of convergence over the original algorithm. The robustness of the proposed controller successfully testing for damping oscillations of two-axis nonlinear single machine infinite bus system. A comparative study for the controller based the classical PI controller have been presented.