Efficient Key Compression in Isogeny‐Based Post‐Quantum Cryptography: A PSO‐WCA Hybrid Optimization Approach

This paper investigates the utilization of a STATCOM to enhance the LVRT capability of wind power plants (WPPs) during grid faults. The STATCOM under investigation is tuned using the Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and a hybrid algorithm of both WCA and PSO. Simulations are conducted in MATLAB programming software, using the SimScape power system toolbox, where two test systems are investigated: a 9 MW WPP and the IEEE 39 bus test system. Performance analysis is done by investigating the ability of the WPPs to ride through grid voltage sags, with the incorporation of the STATCOM, independently tuned using WCA, PSO, and further with the hybrid WCA-PSO algorithm. To confirm the effectiveness of the proposed algorithm, simulation results for the three scenarios are compared. Results show that the LVRT capability of the German power system was met for L-G faults, for the 9 MW test system, whereas during LLL-G faults, the WPP only remained online for WCA and WCA-PSO tuned STATCOM. For the IEEE 39 bus system, the WPPs were able to ride through the LLL-G fault. In all scenarios, the WCA-PSO tuned STATCOM resulted in the least voltage, active, and reactive power overshoots.

Automatic generation control (AGC) and load frequency control (LFC) are very important parts of the power systems in order to provide reliable and quality power supply to the end users. Load variations can lead to the deviation generated power frequency which ultimately can destabilize the entire power system. Here comes the LFC for rescue by maintain the power frequency at desired value. In LFC, area controllers are used to perform this task. In this work, three area interconnected LFC system has been used for simulation purpose. Fractional order proportional integral (FOPI) controllers were used as an area controller in order to mitigate the effect of load variation in power system. In order to optimize the parameters of FOPI controllers, three optimization algorithms namely genetic algorithm (GA), particle swarm optimization (PSO) and water cycle algorithm (WCA) have been used. Performance comparison between optimized FOPI controllers using these algorithms has also been performed. Comparative analysis shows that the WCA optimization performed better than the other two GA and PSO algorithms. LFC system was simulated on MATLAB with the load disturbance of 0.01 p.u. in all areas at the same time.KeywordsLoad frequency controlOptimizationITAEPower systemsWater cycle algorithm

Enhancing low voltage ride through capability of grid connected DFIG based WECS using WCA-PSO tuned STATCOM controller

Background/Objectives: PV array being shaded partially by buildings, trees or passing clouds is common. This makes the P-V curve of the PV system complex with more than one peak. MPPT algorithm capable of consistently detecting the global peak within a short duration of time is essential. Methods/Statistical Analysis: Lately Particle Swarm Optimization (PSO) algorithm has been used for Maximum Power Point (MPP) tracking due to its ability to locate the MPP irrespective of its location in the P-V curve. This paper evaluates and compares the performance of the basic PSO algorithm and the modified PSO algorithms for ten different shading patterns. Findings: The basic PSO algorithm is compared with three modified PSO algorithms - PSO algorithm with random numbers eliminated, PSO algorithm with linearly varying constants and PSO algorithm with fixed maximum iterations. The basic PSO algorithm gives good results but random numbers in the algorithm tends to make the convergence time random for the same shading pattern and makes hardware implementation difficult. The PSO algorithm with random numbers eliminated overcomes this disadvantage and is found to give good results. But the convergence time is a little higher and varies with shading pattern. The PSO algorithm with fixed maximum iterations gives good performance with shorter and fixed convergence time. Application/Improvements: PSO algorithm with fixed maximum iterations thus improves the responsiveness of the algorithm to rapidly changing patterns of shading. Keywords: Maximum Power Point Tracking, Partial Shading, Particle Swarm Optimization, PV Array

A number of recent or novel metaheuristic algorithms have been explored in the subject of optimization; although now not all of these algorithms are as efficient as their creators claim, a few have been confirmed to be a pretty efficient and useful tool for addressing complex optimization problems. On the other aspect, LINGO is a software tool used for linear, nonlinear, optimization problems. This paper's objective is to study how well the LINGO optimizer performs as contrasted to metaheuristic optimization strategies for over current relay coordination. The operation of directional over current relay is demonstrated with the applicability of LINGO optimizer and metaheuristic strategies. In this study, two distinct case studies, which include a mixed overhead line-underground cable and a DER primarily based IEC microgrid benchmark, are tested for comparative evaluation among the LINGO optimizer and metaheuristic strategies. During the first stage of case study I, the crow search algorithm (CSA), a novel metaheuristic technique, was proposed, and its results are contrasted with the most popular conventional techniques: Particle swarm optimization (PSO) and water cycle algorithm (WCA). It reveals that implementing the proposed CSA algorithms improved over all time setting concerning the most common traditional techniques such as PSO and WCA. And, after implementing the LINGO optimizer, it is proven that compared with other metaheuristic optimization techniques, the second one proposed LINGO optimizer provides advanced consequences. In a similar manner, both proposed methods were applied and determined their performances in case study II and once more LINGO proved its strength over the metaheuristic approach.

This paper presents an application of Water Cycle algorithm (WCA) in solving stochastic programming problems. In particular, Linear stochastic fractional programming problems are considered which are solved by WCA and solutions are compared with Particle Swarm Optimization, Differential Evolution, and Whale Optimization Algorithm and the results from literature. The constraints are handled by converting constrained optimization problem into an unconstrained optimization problem using Augmented Lagrangian Method. Further, a fractional stochastic transportation problem is examined as an application of the stochastic fractional programming problem. In terms of efficiency of algorithms and the ability to find optimal solutions, WCA gives highly significant results in comparison with the other metaheuristic algorithms and the quoted results in the literature which demonstrates that WCA algorithm has 100% convergence in all the problems. Moreover, non-parametric hypothesis tests are performed and which indicates that WCA presents better results as compare to the other algorithms.

This article proposes a Kapur-based hybridized Water Cycle and Moth-Flame Optimisation (WCMFO) algorithm that combines a water cycle algorithm (WCA) and moth flame optimisation (MFO) in multilevel thresholding of brain MR image segmentation. The WCMFO algorithm, proposed by Khalilpourazari and Khalilpourazary, gives both WCA and MFO advantages, while avoiding some of the drawbacks of either approach on its own, as demonstrated by faster convergence with broader exploration and exploitation capabilities. Experiments on 10 axial, T2-weighted test images were performed using Kapur entropy as the objective function at a threshold level of m = 2–5. The spiral movement of the behaviour of the moths is used for better exploitation in the WCA to find the global optimum values. WCMFO results, such as objective function value, peak signal to noise ratio, Central processing unit time and standard deviation, are collated and compared with other existing adaptive wind-driven optimization algorithm, adaptive bacterial foraging and particle swarm optimization algorithms. Experimental findings and comparison demonstrated that hybridized WCMFO algorithm was superior to the other algorithms. Moreover, the best segmentation is achieved on grey matter, white matter and cerebrospinal fluid that allows for better clinical decision-making and diagnosis in the medical field. Therefore, the proposed multilevel thresholding-based hybridized WCMFO algorithm is believed to be the most prominent preference for segmenting such complex brain images.

Classification accuracy is strongly affected by the quality of the input features. In recent years, datasets have increased in size and number of features. Analysis of huge datasets can be challenging due to redundant, noisy, and irrelevant features that mayreduce the classifier's performance. Feature selection is a vital process in which the best subset of features from the original dataset is chosen. The feature selection strategy is critical for increasing classification accuracy while decreasing computational costs. This research proposed a method for classifying lip print images by exploiting meta-heuristic methods and optimization-based feature selection methods. It involves four main phases: pre-processing, feature extraction, feature selection, and classification. After pre-processing, the features are extracted from the enhanced image. Meta-heuristic methods such as Genetic Algorithm (GA), ParticleSwarm Optimization (PSO), and Water Cycle Algorithm (WCA) are studied for feature selection using the mean function as the objective function. Finally, the lip print images are classified using a support vector machine (SVM). In this research, the experimental results are compared in terms of accuracy, error, sensitivity, and precision rate between three meta-heuristic methods and the accuracy rate of the proposed method with other algorithms that do not use meta-heuristic methods. The accuracy reached 97.9%, 96.8%, and 95% using WCA, PSO, and GA, respectively.

This paper presents an application of Water Cycle algorithm (WCA) in solving stochastic programming problems. In particular, Linear stochastic fractional programming problems are considered which are solved by WCA and solutions are compared with Particle Swarm Optimization, Differential Evolution, and Whale Optimization Algorithm and the results from literature. The constraints are handled by converting constrained optimization problem into an unconstrained optimization problem using Augmented Lagrangian Method. Further, a fractional stochastic transportation problem is examined as an application of the stochastic fractional programming problem. In terms of efficiency of algorithms and the ability to find optimal solutions, WCA gives highly significant results in comparison with the other metaheuristic algorithms and the quoted results in the literature which demonstrates that WCA algorithm has 100% convergence in all the problems. Moreover, non-parametric hypothesis tests are performed and which indicates that WCA presents better results as compare to the other algorithms.

Optimal operation of reservoirs is one of the most important issues in water resources management. In this study, a novel metaheuristic optimisation algorithm, called the water cycle algorithm (WCA), was used to derive operating policy for a multi-reservoir system. In the first step, the performance of the model was successfully assessed through several benchmark functions. The WCA was then used to derive the optimal operation of four- and ten-reservoir systems. It was then applied to the monthly operation of Golestan and Voshmgir consecutive dams located in Gorganrood basin in the north of Iran. In this way, the objective function was defined as minimising the total deficit for the study period. The WCA results were compared with the results of other developed evolutionary algorithms, including a genetic algorithm, harmony search algorithm, particle swarm optimisation and imperialist competitive algorithm. The results showed that, for cases of four- and ten-reservoir systems, the best solutions achieved by the WCA were 306·3918 and 1172·4197, which had differences of 0·5% and 1% compared with the global optimum solutions, respectively. In addition, it was found that the WCA was superior to other algorithms in calculating the annual deficit of the Golestan–Voshmgir multi-reservoir system.

Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

Side channel attacks are based on side channel information, which is information that is leaked from encryption systems. Implementing side channel attacks is possible if and only if an attacker has access to a cryptosystem (victim) or can interact with cryptosystem remotely to compute time statistics of information that collected from targeted system. Cache timing attack is a special type of side channel attack. Here, timing information caused by cache effect is collected and analyzed by an attacker to guess sensitive information such as encryption key or plaintext. Cache timing attack against AES was known theoretically until Bernstein carry out a real implementation of the attack. Fortunately, this attack can be a success only by exploiting bad implementation in software or hardware, not for algorithm structure weaknesses, and that means attack could be prevented if proper implementation has been used. For that reason, modification in software and hardware has been proposed as countermeasures. This paper reviews the technique applied in this attack, surveys the countermeasures against it, and evaluates the feasibility and usability of each countermeasure. We made comparison between these countermeasure based on certain aspect furthermore.

This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.

Groundwater sustainability is the development and use of groundwater resources to meet current and future beneficial uses without causing unacceptable environmental or socioeconomic consequences. This study is the first time to apply the hybrid optimization technique for solving of managing underground water aquifers, the confined steady flow problems, where a hybrid water cycle - particle swarm optimization WCA-PSO is proposed. In particular, we introduce a novel hybrid algorithm using water cycle algorithm (WCA) and particle swarm Optimization (PSO). The performance of the novel hybrid algorithm WCA-PSO is evaluated to solve 10 benchmark problems chosen from literature. The simulation results and comparison with pure WCA and PSO algorithms confirm the effectiveness of the proposed algorithm WCA-PSO for solving various benchmark optimization functions. Finally, we solve the problem of managing underground water aquifers by WCA, PSO and the hybrid optimization WCA-PSO. The experimental results analysis and statistical tests prove that the hybrid algorithm WCA-PSO overcomes the pure algorithms.

In the lightning monitoring systems, positioning calculation is directly related to the results of the detection accuracy. In this paper, the concept of the particle swarm optimization (PSO) algorithm for lightning location estimation was brought in. The PSO overcome the disadvantages of iterative method, such as the difficulty in finding initial and going to diverge. The numerical simulation results show that: the algorithm can obtained lightning point steadily and accurately, and converge quickly. Therefore, the PSO algorithm on lightning location is feasible. Introduction In the lightning monitoring systems, positioning calculation is directly related to the results of the detection accuracy [1]. The algorithms of lightning location generally use Taylor series and least squares iterative algorithm,which have the shortcomings such as difficult to determine the initial value or easy to diverge. Based on the above, this paper introduces the PSO into the lightning location, and make the numerical simulation and validation. Lightning location Based on Particle Swarm Optimization Algorithm Brief review of the PSO theory and algorithm. The PSO is an effective global optimization algorithms. The basic idea of the PSO is to achieve searching for optimal solutions in complex spatial through collaboration and information sharing among groups of individuals. The PSO adopts Speed-Shift model for action [2]. In each generation population, the particles will track the two extremes: one is the optimal solution the particle itself found so far, namely its extreme [3]; the other is the optimal solution the whole population found so far, namely the global extremum [4]. These two extremes continuously adjust the position of the particle which can be found the optimal solution within a few iterations. PSO can be described as: Let PSO search in an n-dimensional space, the population consists of N particles X = {XX1,XX2, ... ,XXNN}. Each particle location Xii = {xxii1, xxii2, ... , xxiiii} represents a solution of the problem. The particles search for the new solutions by constantly adjusting their positions. Particles by continuously adjust their position (xxiiii) to search for a new solution. Each particle can remember their optimal solutions they have searched for, and the best position (ppgg) the entire particle swarm have went by, which is also the optimal solution searched currently, denoted ppgg. In addition, each particle has a velocity, denoted by Vii = {vvii1, vvii2, ... vviiii}, while the latter two are found, each particle will update their own pace according to Eq. 1. vvii(tt + 1) = wwvi(tt) + cc1RRmm1(ppii − xxii(tt) + cc2RRmm2(ppgg − xxii(tt) (1) xxii(tt + 1) = xxii(tt) + vvii(tt + 1) (2) Where vviiii(tt + 1) represents the i-th particle velocity at t + 1 iterations. ww is the inertia weight, and it can reduce the flight speed of the particle and prevent search divergence; cc1 , cc2 is International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME 2015) © 2015. The authors Published by Atlantis Press 815 acceleration constant, generally take cc1 = cc2 = 2 ;RRmm1,RRmm2 for n × n -dimensional diagonal matrix, the diagonal elements are random number between [0,1]. In addition, the speed of the particles will not be too large, and you can set the speed limit(vvmmmmmm). That is, in Eq. 1 when vvii(tt + 1) > vvmmmmmm, vvii(tt + 1) = vvmmmmmm; when vvii(tt + 1) < vvmmmmmm,vvii(tt + 1) = −vvmmmmmm. Inertia weight ww is given by Eq. 3: w = (wstart − wend) × (MaxDT−iter) MaxDT + wweeiiii (3) Where MaxDT is the maximum number of iterations; Iter is the current iteration number; wstart,wend were initial inertia weight and termination inertia weight, wstart = 0.9,wend = 0.4. PSO implementation steps are as follows: (1) Initialized. Set various parameters PSO algorithm have been involved. (2) Calculate the fitness of each particle (fitness). Store the best place Pbest of each particle and fitness. Choose the best fitness position of the particle from the population as Gbest of populations; (3) Update state of the particles according to Eq. 1 and Eq.2; (4) If the current situation reaches the maximum number of iterations or final result is less than the convergence precision, stop the iterative and output the optimal solution. Otherwise go to step (2). Start Initialize the particle position and initial velocity randomly throughout the search space Calculate the fitness of each particle Update Pbest and Gbest of each particle Update the velocity and position of each particle, according to the Eq. 1 and Eq.2 If the termination condition is satisfied