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In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer's daily activities based on actual time and next day's energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm's performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience. [ABSTRACT FROM AUTHOR]

In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed. [ABSTRACT FROM AUTHOR]

The aim of the optimization economic load dispatch (ELD) problem is to assign the optimal generated power of the thermal units for cost reduction with satisfying the loading of the operational constraints. The ELD is a high-dimensional and non-convex problem that became a more complex problem in the case of optimizing the output generated power of large-scale systems. In this regard, an enhanced version of the Beluga whale optimization (EBWO) is proposed to deal with the ELD of the large-scale systems. Beluga whale optimization (BWO) is an efficient new optimization technique that mimics the behavior of the Beluga whales (BWs) in preying, swimming, and whale fall. However, the BWO may suffer from stagnation in local optima and scarcity of population diversity like other metaheuristics. The proposed EBWO algorithm is presented to render the standard BWO more robust and powerful search by using two strategies including the cyclone foraging motion for boosting the exploitation phase of the optimization algorithm and the quasi-oppositional based learning (QOBL) for improving population diversity. Firstly, Simulations are carried out on seven benchmark functions to prove the validation of the proposed EBWO algorihm compared with five recent algorithms. Then, The performance of the EBWO is checked on 11-units, 40-units, and also 110-unit test systems, and the obtained results of EBWO are compared with other well-known techniques such as the classical BWO, FOX Optimization Algorithm (FOX), Skill Optimization Algorithm (SOA), and Sand Cat swarm optimization (SCSO) as well as the with existing algorithms from the literature including DE, TLBO, MPSO, NGWO, IGA, NPSO, CJAYA, SMA, PSO, PPSO, SSA, MPA, MGMPA, and HSSA. The Numerical results show that the proposed algorithm is very competitive compared with the other reported optimization algorithms in obtaining low fuel costs. [ABSTRACT FROM AUTHOR]

This article describes a technique for improving the performance of an offshore wind farm (OWF), which includes a permanent magnet synchronous generator (PMSG) driven by a variable‐speed wind turbine (VSWT) and connected to the grid through a high‐voltage direct current transmission system (HVDC). Under various network disturbances, the gorilla tropical optimization (GTO) metaheuristic method is used to fine‐tune the proportional‐integral (PI) controller gains for VSC‐based PMSG‐VSWT and VSC‐based HVDC transmission systems. This study compares several strategies for extracting the maximum power from a system while maintaining stability under symmetrical and unsymmetrical fault circumstances. Compared to different algorithms, GTO provides better results and improves system recovery and stability after disturbances. As a consequence, dynamic and transient stability may be enhanced. MATLAB/Simulink is used to implement the control mechanism. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles' mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time‐varying load demand. The uncertainty of DG is represented using photovoltaic‐based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33‐bus and IEEE 69‐bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33‐bus and 69‐bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33‐bus and 69‐bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid analytical and metaheuristic optimization technique for optimal allocation of multiple types of distributed generator (DG) in radial distribution networks. DG integration aims to minimize the total power losses and enhance the voltage stability of the distribution system. The proposed technique is formulated using the analytical technique which utilizes the exact loss formula to calculate the initial DG size at a certain bus. However, the analytical technique may not be proper to allocate multiple DGs types due to the mass of calculations, hence, a metaheuristic optimization technique named Tree Growth Algorithm (TGA) is combined with the analytical technique to find the final solution of optimal DG locations and sizes. The hybridization between the analytical and metaheuristic optimization techniques combines the advantages of both techniques and eliminates the disadvantages. The proposed hybrid Analytical TGA (ATGA) is validated using the stranded radial distribution feeders, IEEE 33‐bus and 69‐bus and practical 94‐bus Portuguese system. A comprehensive comparison between the proposed technique and other competitive optimization techniques is carried out to prove its effectiveness. The result shows that the proposed hybrid ATGA is efficient to allocate the multiple DGs types with minimum power loss and a high convergence rate. [ABSTRACT FROM AUTHOR]

Assessment of power systems voltage stability is considered an important assignment for the operation and planning of power system. In this paper, a voltage stability study using Continuous Power Flow (CPF) is introduced to evaluate the impact of Distribution Generator (DG) on radial distribution systems. On the way to allocate the DG, a hybrid between the Voltage Stability Index (VSI) and Whale Optimization Algorithm (WOA) is developed. The main purpose of using VSI is to find the most sensitive buses for allocating the DG in the system. Hence, Fuzzy logic control with the Normalized VSI (NVSI) and the voltage magnitude at each bus are used to determine the candidate buses. However, the best DG size is calculated using WOA. Four standard radial distribution systems are used in this paper; 12, 33, 69, and 85-bus. The developed hybrid optimization method is compared with other existing analytical and metaheuristic optimization techniques to prove its efficiency. The results prove the ability of the developed method in the allocation of DG. In addition, the influence of the DG integration on enhancing the voltage stability through injecting the proper active and reactive powers is studied. [ABSTRACT FROM AUTHOR]

The integration of several renewable energy sources (RESs), such as photovoltaic generation (PVG) and wind turbine generation (WTG), introduces a significant amount of uncertainty for the optimal planning of the electrical power systems. The optimal power flow (OPF) problem became more difficult to solve when RESs uncertainties were taken into account. In this research, the stochastic OPF problem is solved using an Adaptive Lightning Attachment Procedure Optimizer (ALAPO) with the integration of RESs. The ALAPO is proposed in this study to improve both the exploration and exploitation process of the conventional Lightning Attachment Procedure Optimizer (LAPO). In order to create a number of scenarios that account for the uncertainties associated with load demand, wind speed, and solar irradiation, the scenario based reduction method is utilized. Further, to determine the optimal setting of active power of generators, tap setting of the transformers, reactive power of the capacitor banks, and the voltage profile at the generator buses for each scenario of RESs the objective function considered in this study is to minimize the power loss, voltage deviation, and voltage stability index of the power system. The effectiveness of the proposed algorithm is tested on IEEE 57-bus system. The results in this study show that the proposed ALAPO is superior for solving the OPF with the integration of RESs compared to the conventional methods like LAPO, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimizer (SCSO), Gray Wolf Optimizer (GOW), Whale Optimization Algorithm (WOA), Black Widow Optimization Algorithm (BWO), and Capuchin Search Algorithm (CapSA). [Display omitted] • Integration of several renewable energy. • Optimal power flow (OPF) problem. • Adaptive Lightning Attachment Procedure Optimizer (ALAPO). • Voltage profile at the generator buses is the objective function considered. [ABSTRACT FROM AUTHOR]

In this article, a new hybrid metaheuristic optimization algorithm is proposed to solve the coordination problem of directional overcurrent relays (DOCRs). The proposed algorithm is constructed using hybrid whale optimization algorithm and gray wolf optimizer (HWGO) that enhance the performance and reliability of the traditional whale optimization algorithm (WOA). The proposed method enhances the exploitative phase of the WOA using a leadership hierarchy of the gray wolf optimizer (GWO) to find the best optimum solution. The coordination problem of DOCRs is subject to numerous constraints. The goal function for optimal coordination of DOCRs aims to minimize total operation time for all primary relay without violation in constraints to maintain reliability and security of the electric power system. The effectiveness of the proposed algorithm has been investigated on four different interconnected networks. The results using HWGO algorithm are compared with the original WOA, GWO, and earlier reported results of other optimization techniques. The results prove the viability of the proposed algorithm to solve the DOCR coordination problem and the ability of the proposed algorithm to overcomes the drawbacks and cover the weakness of the conventional WOA. [ABSTRACT FROM AUTHOR]

Abstract The optimization model of Directional Over Current Relays (DOCRs) coordination is considered non-linear optimization problem with a large number of operating constraints. This paper proposes a modified version for Water Cycle Algorithm (WCA), referred to as MWCA to effectively solve the optimal coordination problem of DOCRs. The main goal is to minimize the summation of operating times of all relays when they act as primary protective devices. The operating time of a relay depends on time dial setting and pickup current setting or plug setting, which they are considered as decision variables. In the proposed technique, the search space has been reduced by increasing the C-value of traditional WCA, which effects on the balance between explorative and exploitative phases, gradually during the iterative process in order to find the global minimum. The performance of proposed algorithm is assessed using standard test systems; 8-bus, 9-bus, 15-bus, and 30-bus. The obtained results by the proposed algorithm are compared with those obtained by other well-known optimization techniques. In addition, the proposed algorithm has been validated using benchmark DIgSILENT PowerFactory. The results show the effectiveness and superiority of the proposed algorithm to solve DOCRs coordination problem, compared with traditional WCA and other optimization techniques. Graphical abstract Highlights • Optimization model of directional ov-er current relays coordination. • Modified version for Water Cycle Algorithm. • The main objective is to minimize the operating times of all relays. • Time dial setting and pickup current setting or plug setting. • Balance between explorative and exploitative phases. [ABSTRACT FROM AUTHOR]

Abstract—This article shows a method to optimize the reliability of an electric power system by the introduction of distributed generation. The reliability index of the system is determined as the failure probability of the system. A hybrid method that uses a binary shuffled frog-leaping algorithm and probabilistic load flow is proposed to search a large number of combinations for location and size of biomass power plants and photovoltaic units. The combination with the best performance minimizes the reliability index (probability of failure) of the system. [ABSTRACT FROM PUBLISHER]

• Development of probabilistic and deterministic optimization problem to tune power system stabilizers. • Use of the new directional bat algorithm. • Detailed comparison of probabilistic and deterministic tuning method. • Testing of controller performance under different critical scenarios. The modern power system consists of a large mixture of renewable energy sources (RES), varying and flexible loads, and is also experiencing a situation where a significant number of conventional generators are being replaced by power electronics based sources. All of these factors may lead to a decrease in the small-signal stability (SSS) margin of the system which in turn can cause power system instability. Power system stabilizers (PSSs) are widely used to improve the low-frequency oscillatory stability of power systems. Currently, there are two popular methods to coordinate multiple PSSs to improve SSS: deterministic and probabilistic method. This paper first formulates the problem of coordinating multiple PSSs to improve SSS as a deterministic and a probabilistic optimization problem which is then solved using the new directional bat algorithm. A detailed comparison between the two design methods under different scenarios based on obtained results is carried out afterward. All of the analysis was carried out on a modified IEEE 68 bus system. The obtained results provide key insights as well as the advantages and limitations of the probabilistic and deterministic approach to optimize the PSS parameters. [ABSTRACT FROM AUTHOR]

• Efficient distribution of power among generating units while considering various constraints. • To minimize the overall operating cost of the power system and enhance its efficiency. • The global optimum solution poses a significant challenge. • Enhanced social network search (ESNS) algorithm. The primary objective of Economic Load Dispatch (ELD) is to determine the most efficient distribution of power among generating units while considering various constraints, such as minimum and maximum power output, transmission line capacity, and reserve requirements. By solving the ELD problem, power system operators can minimize the overall operating cost of the power system and enhance its efficiency, which has far-reaching implications for sustainable energy management and resource allocation. However, because of the non-convex nature of the ELD problem, finding the global optimum solution poses a significant challenge. Consequently, several optimization techniques, such as metaheuristics, have been developed in order to address this type of problems. By iteratively exploring the solution space, metaheuristics offer a higher likelihood of finding near-optimal solutions, even in the presence of multiple local optima. This research introduces an enhanced social network search (ESNS) algorithm as an improvement over the existing social network search (SNS) algorithm, aiming to achieve the aforementioned objectives. The core of the SNS algorithm is driven by the social network users' dialogue, imitation, creativity, and disputation moods. The proposed ESNS algorithm builds upon the SNS approach by enhancing its search capability, particularly around the best potential solution. The primary goal is to improve the algorithm's ability to explore global search possibilities while avoiding being trapped in locally optimal solutions. The performance of ESNS has been tested in the 23 benchmark test suits, and its superiority against SNS and other recent algorithms has been verified. Moreover, To evaluate the effectiveness of the proposed ESNS algorithm, it is applied to four standard test systems comprising 11-, 15-, 40-, and 110-unit test systems. The results demonstrate that the ESNS algorithm outperforms other optimization algorithms in terms of solution quality and convergence speed. These findings suggest that the ESNS algorithm holds significant promise as a valuable tool for researchers and power system operators in addressing the economic dispatch problem. Overall, the ESNS technique presents a promising result to this complex challenges. Its capability to handle multiple constraints and its superior performance compared to other recent algorithms make it a valuable addition to the existing set of tools available for solving ELD. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques. [ABSTRACT FROM AUTHOR]

• A linear model for presenting relationship between components of load and its type. • A generalized formulation for system reconfiguration and capacitor allocation. • Simultaneous reconfiguration and capacitor placement under voltage-dependent loads. Efficiently reducing power losses involves various strategies such as opening normally closed switches and closing normally open tie-lines in distribution feeders, and optimally placing shunt capacitors in distribution networks. In this process, the accurate modeling of the demand side is crucial in understanding the behavior of electricity consumers appropriately. It is important to note that loads are not constant; they vary with changes in voltage magnitude, which in turn depend on the type of consumer. Each load can be represented by its constant power, current, and impedance components. In this regard, establishing a clear relationship between these components and consumer types can significantly enhance the flexibility of the network switching or capacitor placement strategy. Therefore, this study aims to mathematically formulate the correlation between load components and consumer types, aiming at establishing an efficient reconfiguration and capacitor allocation formulations. This is achieved by transforming polynomial load formulations into quadratic ones, while establishing mathematical relationships between the quadratic and polynomial models for various load types. The accuracy and convergence time of the proposed model was tested through its application to 16- and 33-bus distribution networks, and the results have been compared with the nonlinear metaheuristic approach. The results demonstrated that the proposed framework can efficiently provide optimal solutions within a shorter computational time as compared to the nonlinear and metaheuristic approaches. [ABSTRACT FROM AUTHOR]

The reliability and effectiveness of today's electrical grids rely heavily on optimal reactive power dispatch (ORPD). The ORPD problem gets more challenging to resolve in the setting of ever-increasingly dynamic and complex systems. To handle the ORPD while also considering the existence of electric vehicles, this research introduces a novel technique: the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm. Byreducing both active power loss and voltage variation, the proposed PSOSHO approach aims at improving the efficiency and reliability of the power grid. Simulation studies on reference power grids, including the IEEE 30-bus and IEEE 57-bus networks, verified its efficacy. Existing metaheuristic optimization techniques are compared using the same restrictions, governing parameters, and data. The results show that the PSOSHO method is trustworthy and effective in resolving the ORPD problem. One of the most pressing issues facing today's electricity grids is how to accommodate the growing number of electric vehicles. Real data on electric vehicles are incorporated in the analyses to obtain a realistic study. The suggested PSOSHO algorithm is a significant step forward in this area, providing a reliable and effective answer to the problem of optimum reactive power dispatch and helping to ensure the long-term viability of power systems in the age of electromobility. • This paper presents a novel optimal energy management of smart grids. • The model includes electric vehicles variability and uncertainties. • A practical study is presented including real data of electric vehicles. • A novel hybrid optimization algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid optimization technique based on analytical and two heuristic methods is developed and applied to determine the optimal allocation of shunt capacitors into distribution systems, aiming to minimize the total power loss and energy cost. The developed technique is a combination of analytical technique based on Loss Sensitivity Factor (LSF) and two recent metaheuristic optimization techniques called Salp Swarm Algorithm (SSA) and Sine Cosine Algorithm (SCA). LSF with a fuzzy logic controller is utilized to determine the candidate buses while the hybrid approach based on SSA and SCA (SSA_SCA) is used to find the optimal capacitor size. The performance of developed technique is investigated using 15-bus, 69-bus, 85-bus, and East Delta Network EDN 30-bus distribution systems. The results of developed technique are compared with those obtained by other comparative optimization methods. The results show the superior of developed technique in determining the optimal allocation of capacitor. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch (ORPD) in electrical networks is essential for the secure and stable operation of the entire power system; it also significantly impacts the economic situation. However, the solution of ORPD is highly complex while considering the multiple variables, time-varying characteristics, dynamic loads such as electric vehicles, and operational constraints. The ORPD problem is classified as a non-linear, non-convex complex problem. Thus, a robust optimization technique should be utilized to solve the ORPD. This paper presents a novel Enhanced Transient Search Optimization (ETSO) technique for the optimal solution of the ORPD problem by integrating electric vehicles (EVs). The proposed ETSO implemented for the optimal solution of ORPD consequently minimize the active power loss and voltage deviation at the load buses. The proposed method is tested and verified on the IEEE 30-bus, the IEEE-57 bus, and IEEE 118-bus systems. The simulation results show the robustness and efficiency of the proposed ETSO optimization in solving the ORPD problem by evaluating and comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. Statistical features of the proposed ETSO algorithm and the results obtained led to an improvement in the performance of the power systems. • Optimal energy management and reactive power dispatch is presented. • The model is solved using a novel enhanced TSO algorithm. • The impact of electric vehicles on the system performance is studied. • Real data of electric vehicles are implemented for obtaining realistic responses. • The results of the proposed method are compared with other models. [ABSTRACT FROM AUTHOR]

• Conventional evaporation rate water cycle algorithm (ERWCA). • The proposed MERWCA improves the performance of ERWCA. • To find the optimal solutions for the coordination problem of directional overcurrent relays. • The proposed MERWCA is verified on the CEC'2017 test suite. • MERWCA is assessed using benchmark DIgSILENT PowerFactory. An optimization algorithm called modified evaporation rate water cycle algorithm (MERWCA) is proposed in this paper to find the optimal solutions for the coordination problem of directional overcurrent relays (DOCRs). The proposed MERWCA improves the performance of a conventional evaporation rate water cycle algorithm (ERWCA) by enhancing the balance between exploitation (search locally) and exploration (search globally) phases to find the best optimum solution. MERWCA includes the Opposition Based Learning (OBL) and Levy Flight (LF) component to address the shortcomings that the original ERWCA may exhibit, to avoid falling on the local optimal and improve the convergence rate. The proposed MERWCA is verified on the CEC'2017 test suite and its performance are compared with those of ten common metaheuristic algorithms (MAs). Both MERWCA and ERWCA are evaluated in the case of non-conventional and conventional relay curves. The feasibility of MERWCA technique is assessed using the conventional IEEE 39-bus meshed distribution test system. The results prove the viability of the MERWCA in solving DOCRs coordination problems in both cases (conventional and non-conventional characteristic relay curve) without any miscoordination between DOCRs pair. Moreover, the reduction ratio in minimizing the total operating using MERWCA reaches 46% with respect to the ERWCA and about 66% using the non-conventional relay characteristic. Finally, the MERWCA is assessed using benchmark DIgSILENT PowerFactory. [ABSTRACT FROM AUTHOR]