Powerful Optimization Algorithm Research Articles

Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm

Combined heat and power economic dispatch (CHPED) problem is one of the most widely handled, optimization problem by researchers in modern power systems. CHPED problem is a complicated, non-continuous, and non-convex optimization problem due to the constraints. Moreover, considering the valve-point loading effect (VPLE), transmission losses (TLs), and prohibited operating zones (POZs) of power-only units as constraints, the complexity of CHPED problem increases. Therefore, a powerful optimization algorithm needs to be introduced to find global solution that meets all constraints. In this paper, a novel adaptive fitness-distance balance based artificial rabbits optimization (AFDB-ARO) is developed to solve CHPED problems. AFDB-based guiding mechanism was implemented to enhance the exploration capability of ARO and to strengthen exploitation-exploration balance. A comprehensive experimental study was realized to prove the performance of the proposed algorithm on the CHPED and benchmark problems. In experimental study between AFDB-ARO variants and ARO on 40 benchmark problems, according to Wilcoxon analysis results, all AFDB-ARO variants outperformed the base ARO, and the best AFDB-ARO variant won victory in 20 of 40 problem and achieved similar results in other 20 problem. In other experimental study, AFDB-ARO algorithm was implemented on the CHPED systems with 4-, 5-, 7-, 24-, 48-, 96-, and 192-units, and fifteen case studies were considered using these systems, VPLE, TLs, and POZs. One of the important points of this study was that POZs were considered for the first time in 96- and 192-units system. The results show that AFDB-ARO achieved the best optimal solution in ten of fifteen cases, was same in one case, and obtained almost same results in four cases compared to the literature. Moreover, the stability of the AFDB-ARO and base ARO algorithms in solving the CHPED problem were tested by performing stability analysis. While the mean success rate, mean iteration number, and mean search time were obtained 87.62%, 353.63, and 2.91 sec of AFDB-ARO, respectively, ARO managed to find the optimal solution in two cases. Thus, the superior performance of AFDB-ARO algorithm is confirmed by experimental studies and analysis against ARO algorithm. The source codes of the AFDB-ARO algorithm (proposed method) can be accessed at this link: https://www.mathworks.com/matlabcentral/fileexchange/136846-afdb-aro-an-improved-aro-algorithm-for-optimization-problem.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

Optimal Power Flow for Hybrid AC/DC Electrical Networks Configured With VSC-MTDC Transmission Lines and Renewable Energy Sources

This article presents the single objective optimal power flow (OPF) formulation incorporating both renewable energy sources, and voltage source converter-based multiterminal direct current transmission lines, simultaneously. To solve the formulated OPF problem, powerful metaheuristic optimization algorithms including adaptive guided differential evolution, marine predators algorithm, atom search optimization, stochastic fractal search (SFS), and fitness-distance balance-based SFS (FDB-SFS) are employed. The performance of the algorithms is tested for the minimization of fuel cost, pollutant emissions of thermal generators, voltage deviation, and active power loss in a modified IEEE 30-bus power network. The simulation results give that FDB-SFS achieved the best results on the fuel cost (786.5361 $/h), the fuel cost with valve point effect (815.6644 $/h), and the fuel cost with emission-carbon tax (820.5991 $/h). In addition, FDB-SFS reduced voltage deviation and active power loss values by 14.2587% and 6.7438% compared to SFS. The nonparametric Wilcoxon and Friedman statistical test results confirmed that FDB-SFS is an effective and robust algorithm that can be used in the optimization of the introduced OPF problem.

Adaptive differential evolution algorithm based on deeply-informed mutation strategy and restart mechanism

Differential evolution is one of the most powerful stochastic real-parameter optimization algorithms currently, and its performance depends heavily on control parameters and mutation strategy. In recent years, methods to select favorable parameters control and mutation strategy when solving various optimization problems have attracted increasing attention. To choose an appropriate mutation strategy and control parameters for a given optimization problem, in this paper, a Adaptive Differential Evolution Algorithm Based on Deeply-Informed Mutation Strategy and Restart Mechanism (ADEDMR) is proposed, and the ADEDMR algorithm has the following characteristics: First, a deeply-informed mutation strategy is proposed, which takes into account the information of suboptimal solutions discarded by selection and inherits the advantages of the powerful “DE/target-pbest/1/bin”, aiming to obtain a better perceptual landscape of the target function and improve the candidate diversity of the trial vector. Second, according to the evolution process, the segmentation method is used to control F, which alleviates the scaling of F in the wrong direction, and makes the newly generated F fit more accurately. Third, a new population restart mechanism is adopted to further enhance population diversity by adaptively enhancing the search ability of hopeless individuals and randomly replacing some inferior individuals with wavelet walks. To evaluate the performance of our proposed algorithm, comparative experiments are conducted on 72 benchmark functions from the CEC2014, CEC2017 and CEC2022 test suites. Experimental results show that the proposed ADEDMR has higher convergence accuracy, better optimization ability when solving high-dimensional complex functions, and is competitive with six recent strong DE variants.

Minimizing energy consumption in UAV assisted NOMA-MEC networks

Mobile edge computing (MEC) technology is seen as a solution to the limitation of computing capability of Internet of Things (IoT) devices. In this paper, we investigate an edge computing framework that supports energy savings for UAV in the case of coexistence of edge computing users and latency-sensitive communication users, who use uplink non-orthogonal multiple access (NOMA) technique to communicate with UAV. The users are grouped to satisfy the delay-sensitive requirements of communication users. Then, the system energy consumption is minimized by optimizing the transmission power and computational resource allocation of the computation users while satisfying the quality-of-service (QoS) of the communication users. Since the proposed problem is nonconvex, it is transformed into a convex problem using the successive convex approximation (SCA) algorithm in this paper. At last, we propose a joint global computational resource and communication power optimization algorithm (GCPA) to minimize the system energy consumption. The simulation results show the effectiveness of the proposed scheme.

Multi-Objective Optimal Power Flow Incorporating Flexible Alternating Current Transmission Systems: Application of a Wavelet-Oriented Evolutionary Algorithm

This article presents a meta-heuristic hybrid algorithm to solve multi-objective optimal power flow (MOOPF) taking into account multi-fuel constraint. Four different objective functions, which comprise total generation cost, emission, real power transmission losses, and voltage deviation, are simultaneously considered in the proposed MOOPF. Furthermore, two types of flexible alternating current transmission system (FACTS) devices are utilized including thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC). Considering all of the aspects referred to above makes the traditional OPF problem more complicated; hence, it is necessary to approach the problem by a powerful optimization algorithm. Toward this end, this article develops a hybrid algorithm oriented to shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO), called hybrid wavelet mutation (WM) based SFLA-wavelet mutation based PSO (HWM2SFLA-PSO). Obtained results validate the effectiveness of the proposed hybrid algorithm in solving different forms of the OPF problem on IEEE test systems compared to those available in the literature. Moreover, the comparison substantiates the superiority of the proposed HWM2SFLA-PSO algorithm in terms of execution time, quality of solution as well as preventing from convergence to local optima, to name but three.

UAV Anti-Jamming Communications With Power and Mobility Control

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain

This paper presents a comprehensive study of various Random Forest (RF) models for predicting increments of pH in groundwater treated by permeable reactive barriers (PRBs). This study uses data collected over 16 years from two PRBs installed in acid sulfate soils. The hyperparameters of the Random Forest have been optimised using three powerful optimisation algorithms: the Whale Optimisation Algorithm, the Tunicate Swarm Algorithm, and the Jellyfish Search Optimizer. The results show that the RF-Jellyfish Search Optimiser was the most accurate model for predicting pH increment in the downgradient. This model could predict pH increments for 80 data points as well as another PRB with a different reactive material. The model was also compared with a finite difference flow model from literature. The results showed that the RF-Jellyfish Search Optimizer was more accurate with the coefficient of determination and root mean square error values of 0.954 and 7.04, compared to 0.942 and 10.27 obtained from the finite difference model. Overall, the novel intelligent technique introduced in this study effectively predicts pH increments in treated groundwater and can also be applied to different PRBs.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems.

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

A review of reactive power optimization algorithm in power system

The paper analyses the significance of reactive power optimization, generalizes the current situation of power system development. Various optimization algorithms were introduced in this paper such as traditional optimization algorithm, intelligence optimization algorithm, including the methods of linear programming, Newtons method, heuristic optimization algorithm, etc. This research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous variables. The purpose of the research is to find the optimal solution of reactive power optimization algorithm, minimize the transport network loss of power system, and improve the quality of users.

FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.

An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

A microgrid is an autonomous electrical system that consists of renewable energy and efficiently achieves power balance in a network. The complexity in the distribution network arises due to the intermittent nature of renewable generation units and varying power. One of the important objectives of a microgrid is to perform energy management based on situational awareness and solve an optimization problem. This paper proposes an enhanced multi-objective multi-verse optimizer algorithm (MOMVO) for stochastic generation power optimization in a renewable energy-based islanded microgrid framework. The proposed algorithm is utilized for optimum power scheduling among various available generation sources to minimize the microgrid’s generation costs and power losses. The performance of MOMVO is assessed on a 6-unit and 10-unit test system. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms for multi-objective optimization.

Reactive power optimization in distribution systems considering load levels for economic benefit maximization

Introduction. The need for electrical energy has been increased sharply due to hasty growth in industrials, social and economic improvements. From the previous studies, it has been agreed that almost 13 % of the total power generated is wasted as heat loss at distribution level. It has been extensively recognized that the node voltage profile along the distribution system can be enhanced under steady state power transfer controlled by proper reactive power compensation. Capacitors have been acknowledged as reactive power compensating device in distribution systems to achieve technical and economical benefits. Novelty of this work is the application of Archimedes optimization algorithm for reactive power optimization in distribution systems so as to obtain an improved solution and also a real 94-bus Portuguese network and modified 12-bus network has been taken and validated for three different load levels which are totally new. Purpose of the proposed work is to maximize the economic benefit by reducing the power loss and capacitor purchase cost at three different load conditions subject to satisfaction of equality and inequality constraints. Methods. The economic benefit has been validated using Archimedes optimization algorithm for three load levels considering three distribution systems. Results. The computational outcomes indicated the competence of the proposed methodology in comparison with the previously published works in power loss minimization, bus voltage enhancement and more economical benefit and proved that the proposed methodology performs well compared to other methods in the literature.

An Optimal Multi-Objective Approach for Distributed Generation and AVR Placement in Distribution Networks Using IPMBSA

Inclusion of automatic voltage regulators (AVRs) and distributed generation units (DGs) in the distribution system (DS) will enhance the system performance if allocated optimally. Optimal allocation of AVRs and DGs is a complex combinatorial problem requiring a powerful optimization algorithm for solving the optimization problem. This paper proposes an improved pareto multi-objective backtracking search algorithm (IPMBSA) based Pareto front approach for optimal siting and sizing of DGs and AVRs. The proposed approach considers DS technical (such as minimizing of voltage deviation, power losses and voltage stability index), cost and emission indices as objective functions while solving the allocation problem. To evaluate the performance of the proposed approach, five different operational scenarios are considered for analysis on IEEE 33-bus, IEEE 69-bus and practical 43-bus Indian distribution test systems. The effectiveness of the proposed approach is highlighted by comparison of results obtained using a multi-objective approach based on NSGA-II and weighted sum method (WSM). Results propounded that allocation of DG along with AVR yielded better DS performance compared to the scenarios where only DG or AVRs are placed in DS.

Whispering Lies to the Eavesdropper: Physical Layer Spoofing Against Eavesdropping Attacks

Recent work in physical layer security focuses on the message transmission between legitimate nodes without revealing the transmitted data to the eavesdropper. However, this gives room for the eavesdropper to adapt its resources to combat with the legitimate nodes. Instead, in this article,we introduce a friendly spoofing aiming at misleading the eavesdropper with a falsifying message signal to mask the original message signal. The effect of such an attack on the legitimate receiver is analyzed. A joint power optimization algorithm is proposed to maximize the spoofing performance while maintaining the communication requirements of legitimate nodes. The numerical results indicate that the proposed system can work under favorable channel propagation cases, and we can provide 4 b/symbols with 99.9% coverage probability with both legitimate and misleading messages.

Numerical Simulation and Experimental Verification of Wind Field Reconstruction Based on PCA and QR Pivoting

Short-term wind forecasting is critical for the dispatch, controllability and stability of a power grid. As a challenging but indispensable work, short-term wind forecasting has attracted considerable attention from researchers. In this paper, Principal Component Analysis (PCA) is applied to Computational Fluid Dynamics (CFD) calculation results for feature extraction and then combined with sparse sensing to achieve the rapid reconstruction of a three-dimensional wind speed field and pressure field. Before reconstruction, the relationship between the reconstruction error and the noise level, and a number of the basis vectors is systematically studied. In the simulation, the wind shear effect is introduced into the inlet boundary condition, and the reconstruction errors of the uniform inlet are 0.21% and 6.46%, respectively, while the maximum reconstruction errors including the wind shear effect are 1.21% and 6.41%, respectively, which verifies the feasibility of applying a PCA-based reconstruction algorithm to a 3D wind field reconstruction. In addition, to solve the time-consuming problem of most optimization algorithms based on a brute-force combinatorial search, an innovative optimization algorithm based on the QR pivoting is investigated to determine the sparse sensor placements. Simulation results show that when the number of sensors is equal to the number of basis vectors, the error of random placement is even 20 times of the optimal placement, which illustrates that QR pivoting is a powerful optimization algorithm. Finally, a wind tunnel experiment of velocity field reconstruction is performed, to verify the practicability of the optimized method based on QR pivoting, and the results indicate that a reasonably high accuracy 3D wind field can be obtained with only 10 sensors (the error of most points is less than 5% and the minimum error is only 0.74%). In general, the proposed algorithm incorporating PCA, sparse sensing and QR pivoting can quickly reconstruct the 3D velocity and pressure fields with reduced measurement costs, which is of great significance for the development of short-term wind forecasting methods.

Covert Communication for Integrated Satellite–Terrestrial Relay Networks with Cooperative Jamming

We explore the influence of cooperative jamming on covert communication for integrated satellite–terrestrial relay networks (ISTRN) in this article, where one full-duplex relay is utilized to support communications and acts as a jammer to confuse eavesdroppers. A cooperative scheme is first proposed to efficiently achieve covert communication in this specific scenario. Further, the probability of detection error (PDE) is derived. Subsequently, the minimum PDE is calculated using the optimal detection threshold. Moreover, a closed-form expression of outage probability (OP) is acquired to quantify the system’s covert performance. Based on this, we design a power optimization algorithm to enhance the effective covert rate under covertness constraints. Lastly, numerical simulation results are given to verify the efficiency of our cooperative jamming scheme when interrupting the eavesdroppers. Notably, an optimal power allocation factor can be obtained, leading to a higher effective covert rate.

Seismic structural control using magneto-rheological dampers: A decentralized interval type-2 fractional-order fuzzy PID controller optimized based on energy concepts

In this paper, a combination of the interval type-2 fuzzy logic controller (IT2FLC) with the fractional-order proportional–integral–derivative (FOPID) controller, namely optimal interval type-2 fractional-order fuzzy proportional–integral–derivative controller (OIT2FOFPIDC), is developed for enhancing the seismic performance and robustness in seismic structural control applications. Based on the energy concepts, a decentralized framework of the OIT2FOFPIDC is proposed for easy and simple implementation in structures during earthquakes. For this purpose, a coot optimization algorithm (COA), as a powerful optimization algorithm, is also applied to adjust the membership functions (MFs), scaling factors, and the main controller parameters. Three controllers, namely optimal type-1 fuzzy proportional–integral–derivative controller (OT1FPIDC), optimal interval type-2 fuzzy proportional–integral–derivative controller (OIT2FPIDC), and optimal proportional–integral–derivative controller (OPIDC), are also proposed for comparison purposes. The seismic performances of the suggested controllers are examined with the evaluation of nine seismic performance indices and different ground accelerations in a 6-story smart structure equipped with two dampers. The robustness of the four controllers in the presence of the stiffness uncertainties is also compared in this study. On average, a reduction of 25.0%, 18.8%, and 18.5% in peak displacement, inter-story drift, and acceleration of stories is obtained for the OIT2FOFPIDC over the OT1FPIDC, respectively. Similarly, these reductions in comparison with the OIT2FPIDC are 16.3%, 13.3%, and 12.0%. Also, these reductions, in comparison with the OPIDC, are 33.3%, 27.8%, and 25.8%. Furthermore, simulation results show that the OIT2FOFPIDC is more robust than the other proposed controllers against uncertainties due to structural stiffness.

Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination rate. We utilized an amalgamation of neural network (NN) with two powerful optimization algorithms, i.e., firefly algorithm and artificial bee colony. For validating the models, we employed the COVID-19 datasets regarding the vaccination rate and the total confirmed cases for 51 states since the beginning of vaccination in the US. The numerical experiment indicated that by considering the vaccinated population, the accuracy of NN increases exponentially when compared with the same NN in the absence of the vaccinated population. During the next stage, the NN with vaccinated input data is elected for firefly and bee optimizing. Based upon the firefly optimizing, 93.75% of COVID-19 cases can be explained in all states. According to the bee optimizing, 92.3% of COVID-19 cases is explained since the massive vaccination. Overall, it can be concluded that the massive vaccination is the key predictor of COVID-19 cases on a grand scale.

Marine predator algorithm with elite strategies for engineering design problems

SummaryMarine predator algorithm (MPA) is a powerful metaheuristic optimization algorithm that shows effective convergence ability on complex benchmark functions. The combination of Brownian and Levy flight distributions directly affects the convergence strategy of MPA. Although MPA has good convergence performance, it is open to improvement as it falls to a local optimum and cannot comprehensively scan the search area during the exploration phase. In this study, MPA has been improved by integrating elite natural evolution and elite random mutation strategies. In addition, these two strategies are combined with Gaussian mutation. The proposed method in this study which is named as elite evolution strategy MPA (EEMPA) has achieved comprehensive scanning of the solution space and considerably reduced the risk of falling into the local optimum trap, with elite strategies. The effect of EEMPA has been tested with the CEC2017 and CEC2019 benchmark functions. EEMPA has been compared with some metaheuristic algorithms frequently used in the literature and gives promising results among the considered optimization methods. Furthermore, EEMPA has been examined for seven well‐known real world engineering problems. When the results are compared with both classical MPA and enhanced MPA methods, EEMPA converges to better than the other methods.