Symbiotic Organisms Search Research Articles

An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.

Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets

Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with the classical K-means algorithm to provide robust and optimum data clustering performance in automatic clustering problems. Moreover, the classical K-means algorithm is sensitive to noisy data and outliers; therefore, we proposed the exclusion of outliers from the centroid update’s procedure, using a global threshold of point-to-centroid distance distribution for automatic outlier detection, and subsequent exclusion, in the calculation of new centroids in the K-means phase. Furthermore, a self-adaptive benefit factor with a three-part mutualism phase is incorporated into the symbiotic organism search phase to enhance the performance of the hybrid algorithm. A population size of 40+2g was used for the symbiotic organism search (SOS) algorithm for a well distributed initial solution sample, based on the central limit theorem that the selection of the right sample size produces a sample mean that approximates the true centroid on Gaussian distribution. The effectiveness and robustness of the improved hybrid algorithm were evaluated on 42 datasets. The results were compared with the existing hybrid algorithm, the standard SOS and K-means algorithms, and other hybrid and non-hybrid metaheuristic algorithms. Finally, statistical and convergence analysis tests were conducted to measure the effectiveness of the improved algorithm. The results of the extensive computational experiments showed that the proposed improved hybrid algorithm outperformed the existing SOSK-means algorithm and demonstrated superior performance compared to some of the competing hybrid and non-hybrid metaheuristic algorithms.

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

SummaryPartial differential equations (PDEs) find extensive applications in geophysics (weather and climate modeling), astrophysics, and quantum mechanics. Solving PDEs by the conventional methods at times becomes difficult due to the complexity involved in these real‐world problems. Thus instead of an exact solution determining an approximate solution is also helpful for solving the problems. Here a model based on radial basis function (RBF) trained with parallel symbiotic organism search (PSOS) optimization is proposed to find out an approximate solution. The PSOS is a nature‐inspired optimization algorithm that ensures better accuracy due to the potential exploration of agents in the search space. Simultaneously, the computational complexity of this algorithm is low due to inbuilt parallelism. Simulation studies are reported for five real applications of PDEs: 2D modified Helmholtz equation, Poisson problem with Dirichlet boundary condition, elliptic PDE equation, and two convection‐diffusion equations. Comparison is carried out with the same RBF model parameters trained with parallel social spider optimization, original symbiotic organisms search algorithm, real coded genetic algorithm, and particle swarm optimization. The proposed model achieves minimum root mean square errors in most of the cases and achieves it with lower run time compared to the existing algorithms.

Optimization solution of congestion problem with FACTS devices using symbiotic organism search algorithm

Optimization solution of congestion problem with FACTS devices using symbiotic organism search algorithm

A metaheuristic-based efficient strategy for multi-unit production planning with unique process constraints

Production planning provides optimal strategic planning to attain economic benefits in the petrochemical industries and helps to maintain competitiveness in the global market. This work proposes an efficient modeling strategy for combinatorial production planning involving unique process constraints in the development stage of the petrochemical industry. The decisions involved are the choice of products to be produced, the product quantity, the suitable process for the selected product, and the number of processing units to be implemented at the operating capacity. The objective is to determine an optimal production plan by maximizing the profit while satisfying all the resource and bound constraints. This work analyzed the strategy in the literature to identify the factors affecting metaheuristic techniques in determining better solutions for production planning involving unique process constraints. The compact and precise strategy proposed in this work reduces the number of decision variables and constraints up to 90% without compromising the rigor of the model. The efficacy of the proposed strategy is demonstrated on four case studies and is solved using eight different metaheuristic techniques, namely (i) Multi-Population based Ensemble of Mutation Strategies Differential Evolution, (ii) Sanitized Teaching Learning Based Optimization, (iii) Artificial Bee Colony, (iv) Dynamic Neighborhood Learning Particle Swarm Optimizer, (v) Sine Cosine Algorithm, (vi) Symbiotic Organisms Search, (vii) Single Phase Multi-Group Teaching Learning Based Optimization with Lévy Flight, and (viii) Harris Hawk Optimization. The results determined by these techniques in solving the case studies using the proposed strategy are better than those provided in the literature. It indicates the suitability of the proposed strategy with metaheuristic techniques and emphasizes the importance of efficient modeling of a problem. The proposed strategy provided up to 5% improvement in profit.

Artificial humming bird with data science enabled stability prediction model for smart grids

Recent advancements in renewable energy provide a clean, alternative source to fossil fuels. Smart grids (SGs) work by collecting data about customer requests, comparing them to current supply data, computing electricity costs, and so on. Because the processes are time-dependent, a dynamic estimate of SG stability is a critical system need. Analyzing fluctuations and disturbances in a dynamic manner is critical for the SGs to work properly. Recent advancements in data science, machine learning (ML), and deep learning (DL) models have facilitated the creation of useful stability prediction models for the SGs environment. In this regard, this study provides an Artificial Hummingbird (AHB) algorithm-based feature selection model for the SG environment with optimal DL enabled stability prediction (AHBFS-ODLSP). The AHBFS-ODLSP model is primarily concerned with the design of an AHB-based feature selection technique. Furthermore, a prediction system based on Multiheaded-Self Attention Long Short-Term Memory (MHSA-LSTM) is being created right now to predict the stability level. Then, using the symbiotic organism search (SOS) optimization technique, the MHSA-LSTM model hyperparameters were adjusted. The AHBFS and SOS algorithm designs have a big impact on how well the MHSA-LSTM model predicts stability. AHBFS-ODLSP model modifications are demonstrated through a number of simulations, and the outcomes are assessed in a variety of ways. The AHBFS-ODLSP method has achieved its maximum performance with a F score of 99.02 %. The AHBFS-ODLSP model outperformed other prediction models, according to a thorough comparative analysis.

Constructing continuum models of acoustic metamaterials via the symbiotic organisms search (SOS) optimization

Based on the symbiotic organisms search (SOS) optimization algorithm, a robust strain gradient (SG) continuum model has been proposed to accurately capture the broadband dispersion relations of one-dimensional acoustic metamaterials. Under the continuous assumption, an unavoidable key step is the Taylor expansion of displacements, which directly influences the accuracy of the corresponding continuum theory. When the wavelength becomes comparable to the periodic characteristic size, the coefficients of Taylor expansions need necessary adjustments due to the discreteness of the microstructure. Thus, the continuum theories still face critical challenges in predicting the broadband dispersion feature. This remains widely open so far. In this study, we attempt to adopt the SOS optimization to determine the optimal Taylor expansion coefficients to guarantee the dispersion diagrams causing the minimal error throughout the first Brillouin zone. The robustness of the SOS-based SG continuum model is demonstrated with three benchmark examples, i.e., the monoatomic, diatomic, and mass-in-mass lattices. Such an attempt of constructing continuum models with the help of optimization tools may shed some new light on continuum mechanics of structure media.

Optimization Model for Maintenance Planning of Loading Equipment in Open Pit Mines

Maintenance plays a significant role in operating costs in the mining industry. Improving this matter controls maintenance costs and enhances productivity and production effectively. Shovels are one of the most widely used loading machines in non-continuous activities. Thus, evaluating and optimizing their availability is one of the essential solutions to achieving high productivity and cost reduction. This paper presents a mathematical programming model to maximize availability and minimize the total expected costs. We programmed the proposed nonlinear planning model using the Symbiotic Organisms Search (SOS) meta-heuristic algorithm in Matlab software. It determines the optimal maintenance intervals for different parts of the shovel. The maintenance benefit analysis approach selects various maintenance activities in optimal maintenance intervals. The model is implemented in a practical case study, Chadormalu Iron Mine, to evaluate its performance. The failure distribution matches the Weibull distribution function. The computational results show the efficiency of the presented approach.

Synthesizing of concentric circular antenna arrays by using a combination of ant lion optimizer and sequential quadratic programming

AbstractAntenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO‐SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition‐based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs.

Concurrent Scheduling of Machines and AGVS in Multi-Machine FMS with Alternative Routing Using Symbiotic Organisms Search Algorithm

This paper addresses machines and automated guided vehicles (AGVs) simultaneous scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) to produce the best optimal sequences for the minimization of makespan (MKSN). This problem is highly complex to solve because it involves the selection of machines for job operations (jb-ons), the sequencing of jb-ons on the machines, the assignment of AGVs, and associated trips such as AGVs’ deadheaded trip and loaded trip times to jb-ons. This paper offers a nonlinear mixed integer programming (MIP) formulation for modeling the problem and the symbiotic organisms search algorithm (SOSA) for solving the problem. For verification, a manufacturing company’s industrial problem is employed. The results show that SOSA outperforms the existing methods and the Jaya algorithm, and using alternate machines for the operations can reduce the MKSN.

Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods

In this paper, a metamodel-based approach involving simulation data collection and data-driven techniques was used to forecast and optimize heating and cooling loads in three different climates in Morocco. The metamodel method is gaining popularity as it offers a better balance between accuracy and calculation time. In addition, a wrapper method was used as a feature selection approach to find the best feature subsets in order to reduce models’ complexity. Therefore, the performances of eleven state-of-the-art algorithms including evolutionary-based algorithms, swarm intelligence-based algorithms and human-based algorithms, coupled with a learner such as Artificial Neural Networks (ANNs) and Support vector machine (SVM), were assessed. Hybrid model based on League championship algorithm (LCA), Discrete symbiotic organisms search (DSOS) algorithm, Particle swarm optimization (PSO) algorithm showed better results in terms of accuracy and reduction of feature input parameters. Indeed, the best desired performances were obtained with LCA-SVM for cooling load, DSOS-SVM for heating load, PSO-ANN for both heating and cooling loads. On the other hand, to optimize the annual thermal load, the NSGA-II algorithm was used. Results showed a reduction of 68% of the total annual load compared to the base case in Meknes city, 73% in Ifrane and 67% in Marrakech. The solution that reflects a compromise between the two objective functions (i.e., cooling load and heating load) gives better results in terms of CO2 emissions reduction in all climates evaluated, except in the cold climate.

Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning

The effective prediction of stock market prices and trends is a critical topic in financial research for investors and stakeholders who wish to increase their return on investment. Motivated by highly unstable stock market targets and the continuous trading system that has been launched by Taiwan’s stock market, this work develops an intelligent candlestick forecast system (ICFS), which combines the advantages of three forecasting techniques — single-point, interval value, and trend forecasting. The ICFS is simple and intuitive, using a sliding-window metaheuristic-optimized multi-output least squares support vector regression hybrid model scheme to forecast simultaneously four types of price information that are represented on a candlestick chart. Three parameter-free metaheuristic algorithms, namely teaching-learning-based optimization (TLBO), symbiotic organisms search (SOS), and forensic-based investigation (FBI), are in turn used to optimize the hyperparameters of the multi-output least squares support vector regression (MLSSVR) model, to increase its accuracy and stability in predicting stock prices. Benchmark data from relevant studies are used to compare the forecast performances of previously developed and the three proposed hybrid models. Among all tested models, the FBI-MLSSVR greatly outperforms the others in forecasting Standard & Poor’s (S&P) and National Association of Securities Dealers Automated Quotations (NASDAQ) indexes, Taiwan’s exchange-traded funds, and major listed construction companies. A floating dollar-cost averaging strategy for maximizing returns while minimizing investment risks is proposed and compared with traditional dollar-cost averaging and the buy-and-hold strategy. The profitability of the predictions made using the proposed system is verified against actual stock market operations. The ICFS is packaged as a visualized expert system and independent application program for the convenience of users, who can intuitively run it and interpret its forecasts. Therefore, this study is highly practical by providing stakeholders with an easy-to-use and accurate predictive tool.

A Robust Protection Scheme for Multimicrogrids Using Fault Current Limiter

The operation of multimicrogrids (MMGs) can cause significant impact on power system network, especially changes in level and direction of fault current. The fault current level may exceed rating of existing directional overcurrent relays (DOCRs) that lead to sympathetic tripping or fail to trip DOCRs in the network. It is expensive and extremely challenging to upgrade or replace the existing DOCRs in the network. If not properly addressed, this issue may hinder the deployment of MMGs in to the network. Hence, this article proposes a robust protection scheme for DOCRs coordination in MMGs without replacing existing DOCRs in network. First, the proposed scheme calculates optimal settings of DOCRs in all stand-alone microgrids of MMG during islanded mode. Then, optimal values of fault current limiters are obtained using calculated optimal settings. The main advantage of proposed scheme is that it provides one set of DOCRs settings valid for all operating modes of MMG. In this work, the DOCRs coordination problem is formulated as nonlinear problem and solved using proposed hybrid approach symbiotic organisms search (SOS) and nonlinear programming (NLP). The initial solution is determined by SOS and final solution is obtained using NLP. Further, the proposed scheme is examined during gird connected and islanded modes in two different configurations (series and parallel) of MMG, which are not stated in previous literature. Finally, simulation results are validated using numerical relay and real time digital simulator, which are interfaced through voltage and current amplifier to execute hardware-in-loop test bed.

Simulation Optimization for Location and Allocation of Emergency Medical Service

Emergency medical services are an essential element in the modern healthcare system. Health care services are the most important because they play an important role in saving people's lives and reducing rates of mortality and morbidity. Especially during the covid-19 pandemic and the new normal era makes this problem very interesting to discuss. For this reason, this study tries to overcome the problem location and allocation of MES by using a combination of metaheuristics and simulation. The approach taken to overcome these challenges is developing Symbiotic Organisms Search algorithm and then use the simulation method to validation the result. The transition of the ambulance system from a centralized to decentralized system by using the M-SOS algorithm, found that to shorten the response time to 9 minutes, need to combine the 5 core bases with about 12 potential bases. From the simulation scenarios tested, the total number of ambulances involved in the proposed system is 16 units. So it can be concluded that involving several potential bases can produce a short response time.

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches. Six swarm intelligence algorithms, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), symbiotic organisms search (SOS), salp swarm algorithm (SSA), slime mould algorithm (SMA), and Harris hawks optimization (HHO) have been applied and compared to optimize the LSSVM parameters. For this purpose, a literature dataset (891 datasets) of different types of soils has been used to design and evaluate the proposed models. The input variables in all of the proposed models included confining stress, deviator stress, unconfined compressive strength, degree of soil saturation, soil moisture content, optimum moisture content, plasticity index, liquid limit, and percent of soil particles (P #200). The accuracy of the proposed models was assessed by comparing the predicted with the observed of Mr values with respect to different statistical analyses, i.e., root means square error (RMSE) and determination coefficient (R2). For modeling the Mr of subgrade soils, percent passing No. 200 sieve, optimum moisture content, and unconfined compressive strength were found to be the most significant variables. It is observed that the performance of LSSVM-GWO, LSSVM-SOS, and LSSVM-SSA outperforms other models in predicting accurate values of Mr. The (RMSE and R2) of the LSSVM-GWO, LSSVM-SSA, and LSSVM-SOS are (6.79 MPa and 0.940), (6.78 MPa and 0.940), and (6.72 MPa and 0.942), respectively, and hence, LSSVM-SOS can be used for high estimating accuracy of Mr of subgrade soils.

A novel artificial intelligence-aided system to mine historical high-performance concrete data for optimizing mixture design

Designing proper HPC mixtures is an onerous task, as many different chemical substances are used to produce HPC of differing specifications and performance characteristics. In this study, a novel, artificial-intelligence-aided system is developed to effectively minimize the cost of producing HPC of variable compressive strength, workability, and durability requirements. In the first phase, a symbiotic organism search-optimized least squares support vector machine (SOS-LSSVM) is developed using historical data to predict HPC properties to verify that these properties conform with requirements. In experimental testing conducted for this study, the SOS-LSSVRM achieved MAPE values of 17.44% 16.31%, and 22.70%, respectively, for predicting compressive strength, workability, and durability. In the second phase, symbiotic organism search (SOS) is used to identify the lowest-cost HPC mixture that attains the specified mechanical requirements. The results demonstrate that the proposed artificial intelligence-aided system is effective in guiding the economical design of HPC mixtures that comply with ACI and DMDA standards.

Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning

Reinforced concrete shear walls are used in many structural systems to resist earthquake loading. In recent earthquakes, shear wall buildings have tended to perform well. Modern building codes include provisions concerning shear capacity, which are recognized for their effectiveness. Studies have demonstrated that the American Concrete Institute (ACI) 318-19 provision uses a low safety factor and does not cover high-strength concrete shear walls whereas the Eurocode 8 provision is overly conservative. A rational method for predicting shear wall capacity could be used as an alternative to the simplified provision in the codes. Nevertheless, the use of rational methods may present some difficulties for structural engineers because they require an iterative calculation to determine the peak strengths of shear walls. Accordingly, an appropriate data-driven machine learning scheme that accurately determines shear capacity is needed. Three experimental cases that involve various input variables are adopted herein to train single models and ensemble models. Numerical analytics show that the best result is achieved by using extreme gradient boosting (XGBoost), which involves conventional parameters and synthetic parameters that are inspired by the ACI shear wall strength equation. Subsequently, two metaheuristic optimization algorithms are used to fine-tune the hyperparameters of the generally recognized XGBoost. Two proposed metaheuristically hybrid models, jellyfish search (JS)-XGBoost and symbiotic organisms search (SOS)-XGBoost, outperform the ACI provision equation and grid search optimization (GSO)-XGBoost in the literature in predicting the nominal capacity of reinforced concrete shear walls in buildings. Metaheuristics-optimized machine learning models can be used to improve building safety, simplify a cumbersome shear capacity calculation process, and reduce material costs. The systematic approach that is utilized herein also serves as a general framework for quantifying the performance of various mechanical models and empirical formulas that are used in design standards.

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems.

Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: a case study

A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: a case study

A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization

Multi-objective (MO) problems are complex and challenging to solve due to multiple conflicting objectives and incommensurate constraints. Heat Transfer Search is a recently introduced thermodynamic laws-based algorithm for solving many single objective problems. But it faces criticism due to its shortcomings including local optima trap, computational complexity, and reliability issues while solving MO problems. Decompositions is a simple yet efficient framework that aids in resolving fitness assignments and diversity maintenance issues of MO problems. It is also popular for reducing computational complexity and improving solution quality. Therefore, this work proposes and investigates a novel, simple, and robust Decomposition-based Multi-Objective Heat Transfer Search (MOHTS/D) for solving real-world structural problems. To achieve the Pareto optimal solutions and to confirm their coverage behaviour, the evenly generated weight vectors sorting and Euclidean distance strategies were employed in the proposed posteriori method. The performance of MOHTS/D is investigated through eight constrained widely accepted benchmarks. The results contrast with the MOHTS, MO evolutionary algorithm based on decomposition, MO passing vehicle search, MO slime mould, MO symbiotic organisms search, and MO multi-verse optimization. The efficacy of MOHTS/D is evaluated based on hyper-volume, coverage, inverted generational distance, pure diversity, spacing, spread, coverage Pareto front, diversity maintenance, generational distance, and runtime metrics. The results reveal that MOHTS/D is a robust optimization approach compared to others for optimizing real-life structural problems. MOHTS/D was able to find the optimal solution with minor computational complexity, and the obtained solutions demonstrate a better convergence, coverage, diversity, and spread behaviour over Pareto fronts