Multi-verse Optimizer Research Articles

Clustering based EO with MRF technique for effective load balancing in cloud computing

PurposeCloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.Design/methodology/approachVMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.FindingsThe proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.Originality/valueUser’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Dynamic Leader Multi-Verse Optimizer (DLMVO): A New Algorithm for Parameter Identification of Solar PV Models

To efficiently extract the model parameters of photovoltaic (PV) modules, this paper proposed an identification method based on the Dynamic Elite-Leader Multi-Verse Optimizer (DLMVO) algorithm. An adaptive strategy was used to control parameters based on population evolution rate and aggregation rate to balance the exploitation and exploration to avoid the search falling into local optimization. In addition, this paper proposed a dynamic elite-leader-based variation strategy to enhance the probability of variation success and improve merit search speed. This proposed algorithm was applied to the parameter identification of two different PV modules and validated using six existing methods in the literature for comparison. The experimental results show that the DLMVO algorithm significantly reduced the standard deviation of the three models compared with the standard deviation of the MVO algorithm, the single diode decreased by nearly 40%, the single-component model decreased by about 28%, and the double diode exhibited the best effect, which decreased by 83%.

An Improved Scheduling of Vehicle Routing in Smart Cities by Multiverse Optimization

Abstract One of the research problems is the vehicle routing problem (VRP) in smart cities for smooth movement of the vehicles and less traffic in the smart cities. The dynamic vehicle routing structure is a problem where a time frame is given for each vehicle to reach the destination, and this is classified as VRP with time windows. The Solomon’s datasets are utilized for this vehicle routing problem with time windows (VRPTW). The goal is to calculate the least number of vehicles using two variants of multiverse optimizer. The two variants of multiverse optimizer are compared with the other techniques in terms of the travelled distances, the number of vehicles and the computational time are analyzed, and the effectiveness is calculated for all the proposed models for the VRPTW problem.

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.

Optimizing time and cost simultaneously in projects with multi-verse optimizer

Optimizing time and cost simultaneously in projects with multi-verse optimizer

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

Metaheuristic Algorithms to Enhance the Performance of a Feedforward Neural Network in Addressing Missing Hourly Precipitation

This research study investigates the implementation of three metaheuristic algorithms, namely, Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), and Moth-Flame Optimisation (MFO), for coupling with a Feedforward Neural Network (FNN) in addressing missing hourly rainfall observations, while overcoming the limitation of conventional training algorithm of artificial neural network that often traps in local optima. The proposed GWOFNN, MVOFNN, and MFOFNN were compared against the conventional Levenberg Marquardt Feedforward Neural Network (LMFNN) in addressing the artificially introduced missing hourly rainfall records of Kuching Third Mile Station. The findings show that the proposed approaches are superior to LMFNN in predicting the 20% hourly rainfall observations in terms of mean absolute error (MAE) and coefficient of correlation (r). The best performance ANN model is GWOFNN, followed with MVOFNN, MFOFNN and lastly LMFNN.

Estimation of properties of ultra great workability concrete by optimized ANFIS models

The mechanical properties of the ultra-great workability concrete (UGWC) are deeply related to the weights of components, curing period and condition, and occasionally property of admixtures. This study aimed to appraise the usefulness of the adaptive neuro-fuzzy inference system (ANFIS) technique for forecasting the compressive strength of UGWC and enhancing the accuracy of the literature. To outline the forecasting process, two improved ANFIS were suggested, in which determinative variables of them were determined by metaheuristic algorithms named imperialist competitive algorithm (ICA) and multi-verse optimizer (MVO) algorithms. For this purpose, 170 data samples were collected from published literature separated accidentally for the train and test phase. The calculated performance criteria for proposed ANFIS models demonstrate that both ICA-ANFIS and MVO-ANFIS models can result in justifiable workability for fc of the UGWC prediction procedure. The MVO-ANFIS model could outperform ICA-ANFIS regarding all criteria. For instance, the value of R2 and VAF for the ICA-ANFIS model are roughly smaller than the MVO-ANFIS model, at 0.9012 and 90% in the training dataset and 0.8973 and 89% in the testing stage, respectively. While the best values of criteria have belonged to the MVO-ANFIS model, with R2 at 0.937 and 0.944 for the train and test phases, respectively. Overall, the hybrid MVO-ANFIS model can obtain higher workability than ICA-ANFIS and literature (R2 at 0.801), where causes are recognized as the proposed model.

A two-archive multi-objective multi-verse optimizer for truss design

Multi-objective structure optimization is a complex design issue that involves dealing with multiple conflicting objectives and various constraints. Although metaheuristics have been successful in resolving such issues, their stochastic nature and limitations can make implementation challenging. To address this challenge, an efficient and powerful optimizer called the multi-objective multi-verse optimizer (MOMVO) has been proposed in this study. The MOMVO algorithm is based on a two-archive concept focused on convergence and diversity, respectively, and is termed MOMVO2arc. The effectiveness of MOMVO2arc was evaluated by applying it to five conventional planar and spatial real-world structural optimization problems. These problems had conflicting objectives, such as minimizing structural mass and minimizing maximum nodal deflection, while imposing safety and size constraints related to stress on components and discrete cross-sectional areas. The results were then compared with those of seven advanced optimization techniques, including MOEA/D and NSGA-II, using four globally accepted performance indicators, including Hypervolume, Inverted Generational Difference, Spacing to Extent matrices, and the qualitative behavior of the best Pareto-front plots that each algorithm can produce. The comparative analysis and average Friedman rank test revealed that MOMVO2arc was more effective in solving large structure optimization problems with less computation time. The suggested approach not only found and maintained more Pareto-optimal sets but also achieved good diversity and convergence in both the decision and objective spaces. Therefore, MOMVO2arc can be a promising tool for resolving complex multi-objective structure optimization problems.

Noise-immune state of charge estimation for lithium-ion batteries based on optimized dynamic model and improved adaptive unscented Kalman filter under wide temperature range

Due to diverse vehicle driving conditions, it is difficult to ensure that lithium-ion batteries operate at a fixed ambient temperature. Meanwhile, the environmental noise accompanying vehicle driving can also affect the data sampling accuracy of batteries. To achieve a high-robustness state of charge (SOC) estimation for lithium-ion batteries at different ambient temperatures with noise effects, a noise-immune SOC estimation method under a wide ambient temperature range is proposed. First, based on the moving window principle, a high-precision fitting model of the open-circuit voltage is established. Second, based on the Multi-Verse Optimizer, a new dynamic model parameter identification method is proposed, while the complete optimized dynamic battery model is established relying on the data of Dynamic Stress Test at different temperatures. Third, to enhance the SOC estimation accuracy and stability, based on adaptive theory and matrix diagonalization theory, the unscented Kalman filter is improved. Finally, the effectiveness and robustness of the proposed method are validated under two other working conditions with random noise added at various temperatures. Under every set of working conditions at all temperatures, the SOC estimation results can maintain stability after converging to the reference SOC, while root mean square errors and mean absolute errors under all cases do not exceed 1.5 %.

Solar PV Combined Efficient Torque Control of BLDC Motor Using Salp Swarm Optimization

This study provides an efficient torque and speed control method for brushless DC (BLDC) motor utilizing a fractional order proportional integral derivative (FOPID) controller based on the multiverse optimization algorithm and salp swarm optimization (SSO). When combined with a solar photovoltaic (PV) system and a voltage boosting architecture, standard PID controllers regulated the BLDC drive parameters due to their simplicity and improved steady-state performances. A solar PV system with a specific power range is chosen for the best output solution on the output side. The MPPT method employed is the incremental conductance approach for tracking maximum power from the source. However, it has a problem with unpredictability owing to load changes. The PID controller tuning also relates to the structure’s parametric uncertainties. Therefore, precise control methods may be offered with the aid of the FOPID controller to address the aforementioned issues. Simulation results of the proposed MVO and SSO-based FOPID controller for BLDC speeds run in the Matlab Simulink platform. To confirm the adaptability of the suggested control scheme for BLDC motor, the outcomes are interrelated using genetic algorithm, gray wolf, and PSO-based FOPID controllers.

A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

SummaryCloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP‐hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane‐inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane‐inspired approach to introduce a conventional membrane‐inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane‐inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.

Estimation of contamination release histories using meshless radial point collocation method and multiverse optimization

Abstract Contaminants in groundwater may enter through various sources which are required to be identified for informed decision-making regarding remediation. In early stages of contamination of an aquifer, the sources of contamination are generally unknown. To estimate the unknown release histories at potential sources, the governing equations of contaminant transport are solved backwards in time which renders the inverse problem as ill-posed. Furthermore, due to measurement or operational errors, observed breakthrough curves can be noisy which can induce uncertainty. Simulation-optimization (SO) techniques are generally used for solving the inverse model. Nevertheless, swarm population-based optimization algorithms may get trapped in local optima in the nonlinear search space related to the problem. In this study, SO model based on meshless radial point collocation method (RPCM) and multiverse optimizer (MVO) is proposed. MVO implements gradual transformation from global exploration to local exploitation phase which helps in better estimation of unknowns. The performance of RPCM-MVO-SO model is compared with two other SO models developed by combining RPCM with grey wolf optimizer and particle swarm optimization in two case studies. The proposed model performs better compared to the other two SO models considered which suggests the efficacy of the model for release history estimation in groundwater.

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information.

The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.

An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

SummaryOne of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi‐objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta‐heuristic algorithms: Moth‐Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi‐Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.

Multi-Objective Optimization-Based Approach for Optimal Allocation of Distributed Generation Considering Techno-Economic and Environmental Indices

Distribution networks have entered a new era with the broad adoption of the distributed generation (DG) allocation as a practical solution for addressing power losses, voltage variation, and voltage stability. The primary goal is to enhance techno-economic and environmental characteristics while meeting the limitations of the system. In order to allocate DGs in active distribution networks (ADNs) efficiently, this study demonstrates two optimization methods inspired by nature: ant lion optimization (ALO) and multiverse optimization (MVO). Various multi-criteria decision-making (MCDM) methods are used to find the best possible solution among the different alternatives. On the IEEE 33- and 69-bus active distribution networks, the proposed ALO was shown to be effective and produces the highest loss reduction in the IEEE 33- and 69-bus systems at 94.43% and 97.16%, respectively, and the maximum voltage stability index (VSI) was 0.9805 p.u and 0.9937 p.u, respectively; moreover, the minimum voltage deviation (VD) and annual energy loss cost for the given test systems was 0.00019 p.u and 3353.3 PKR, which shows that the suggested method can produce higher quality results as compared to other methods presented in the literature. Therefore, the proposed ALO is a very efficient, effective, and appealing solution to the optimal allocation of the distributed generation (OADG) problem.

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multi-objective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system.

Underwater image illumination estimation via an evolving extreme learning machine by an improved salp swarm algorithm.

Underwater images have chromatic aberrations under different light sources and complex underwater scenes, which can lead to the wrong choice when using an underwater robot. To solve this problem, this paper proposes an underwater image illumination estimation model, which we call the modified salp swarm algorithm (SSA) extreme learning machine (MSSA-ELM). It uses the Harris hawks optimization algorithm to generate a high-quality SSA population, and uses a multiverse optimizer algorithm to improve the follower position that makes an individual salp carry out global and local searches with a different scope. Then, the improved SSA is used to iteratively optimize the input weights and hidden layer bias of ELM to form a stable MSSA-ELM illumination estimation model. The experimental results of our underwater image illumination estimations and predictions show that the average accuracy of the MSSA-ELM model is 0.9209. Compared to similar models, the MSSA-ELM model has the best accuracy for underwater image illumination estimation. The analysis results show that the MSSA-ELM model also has high stability and is significantly different from other models.

A parallel workflow framework using encoder-decoder LSTMs for uncertainty quantification in contaminant source identification in groundwater

Groundwater contamination is a major problem and it can be contaminated by natural or manmade activities. Contaminant sources cannot be identified promptly after a contamination event due to slow groundwater movement in aquifers. To limit the further spread of the plume and to take appropriate remedial measures, it is essential to determine the release rates associated with source locations as soon as a contaminant is detected in one of the observation wells in the aquifer. Conventional techniques for contaminant source identification such as the simulation–optimization (SO) uses a simulation model, which is based on estimated or, measured hydrogeological parameters, for replicating the aquifer response to contamination at source locations. However, the associated intrinsic uncertainty in the parameters can affect the estimated release histories at the source locations. To assess confidence in the results, it is important to consider stochasticity in the hydrogeological parameters for contaminant release history estimation requiring numerous runs of the SO model for corresponding breakthrough curves. The present study proposes a novel surrogate simulation optimization (SSO) framework which utilizes an Encoder-Decoder Long Short-Term Memory (ED-LSTM) based surrogate model along with Multiverse Optimization (MVO) for contaminant source identification. The comparison of ED-LSTM with other surrogate models shows its better accuracy and significantly low computational time requirement compared to the simulation model. To enhance the computational efficiency further, the SSO model is embedded in a parallel workflow for the quantification of uncertainty associated with the inverse modelling. The study shows that the proposed workflow can be efficiently used for uncertainty quantification in contaminant source identification due to stochastic parameters with a reduced computational cost.

Optimization of micro grid with distributed energy resources using physics based meta heuristic techniques

AbstractRecently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.