Social Group Optimization Research Articles

A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time-cost-quality-CO2 emissions of generalized construction projects

PurposeMost of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.Design/methodology/approachIn this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.FindingsThe efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.Originality/valueThis paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.

Neutrosophic goal programming technique with bio inspired algorithms for crop land allocation problem

In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and indeterminate factors like seed growth and suitable fertilizer. In order to address this problem, other researchers have used fuzzy and intuitionistic fuzzy optimization approaches, which did not include the indeterminacy membership functions. However, the neutrosophic optimization technique addresses the problem by using individual truth, falsity, and indeterminacy membership functions. So, to improve the optimal solution, the Neutrosophic Goal Programming (NGP) problem with hexagonal intuitionistic parameters is employed in this study. The membership functions for truth, indeterminacy, and falsity are constructed using hyperbolic, exponential, and linear membership functions. Minimizing the under deviations of truth, over deviations of indeterminacy, and falsity yields the NGP achievement function, which is used to attain optimal expenditure, production, and profit under the constraints of labour, land, food requirements, and water. Bio-inspired computing has been a major research topic in recent years. Optimization is mostly accomplished through the use of bio-inspired algorithms, which draw inspiration from natural behaviour. Bio-inspired algorithms are highly efficient in exploring large solution spaces, and helps to manage trade-offs between various goals, and providing the global optimal solution. Consequently, bio-inspired algorithms such as Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO) are employed in the current work to determine the global optimal solutions for the NGP achievement function. The data for the study was collected from the medium-sized farmers in Ariyalur District, Tamil Nadu, India. To illustrate the uniqueness and application of the developed method, the optimal solutions of the suggested method are compared with Zimmermann, Angelov, and Torabi techniques. The proposed technique demonstrates that the bioinspired algorithms’ optimal solution to the neutrosophic goal is superior to the existing approaches.

Optimized Feature Selection Using Modified Social Group Optimization

Optimized Feature Selection Using Modified Social Group Optimization

Hybridization of Social Group Optimization and Past Present Future Algorithm and its Application for Software Cost Estimation

Hybridization of Social Group Optimization and Past Present Future Algorithm and its Application for Software Cost Estimation

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals’ evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time.

Brain Tumors Detection Using Deep Learning (SGO and SE Algorithm)

Brain abnormality is the harsh illness among humans and is usually diagnosed with medical imaging procedures. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and Diffusion-Weighted (DW) modalities. The combination of the Social- Group-Optimization (SGO) and Shannon’s Entropy (SE) supported Multi-threshold is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES2015 and BRATS (2013 and 2015), and also clinical MRI mages obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with related segmentation trials employed. The performance of the ANFIS is validated with other classifiers, such as Decision-Tree, Random-Forest and KNN. The ANFIS classifier obtained an accuracy of 97.04% on the used DW modality and accuracy of 96.67% on the Flair MR images.

Efficient Scheduling of Charger-UAV in Wireless Rechargeable Sensor Networks: Social Group Optimization Based Approach

Efficient Scheduling of Charger-UAV in Wireless Rechargeable Sensor Networks: Social Group Optimization Based Approach

Hybridization of Social Group Optimization and Differential Evolution Algorithm for Solving Speed Reducer Design Problem

Objectives: The objective of this study is to present a hybrid approach named SGO-DE, which combines the Social Group Optimization (SGO) algorithm and differential evolution (DE), aiming to balance exploration and exploitation capacities to improve the accuracy of the optimization algorithm in finding optimal solution for speed reducer design problem. This hybrid approach is simulated for a speed reducer mechanical engineering design problem and the results are compared to several other state-of-the-art optimization algorithms. Method: To improve the exploration and exploitation of SGO, in its acquiring phase Differential Evolution (DE) is introduced. The individual candidate solutions derived from the Improving phase of SGO tries to acquire better values using DE. This helps in striking a better balance between exploration and exploitation, there by achieving improved optimal values and not getting trapped in local optima. The performance of the SGO-DE method is then evaluated and compared to other optimization algorithms through experimentation on the speed reducer design challenge. Findings: The findings of this study indicate that the SGO-DE hybrid approach outperforms other state-of-the-art algorithms by a significant margin in terms of optimization results. The numbers of function evaluations (FEs) significantly go low as less as 6000 compared to other state-of-the algorithms. The comparison demonstrates the efficacy of the SGO-DE method in enhancing solution quality and speeding up execution. Novelty: The novelty of this study lies in the development a hybrid approach of SGO and DE which is efficient in achieving competitive performance in less numbers of function evaluation in speed reducer design problem. This hybridization can strike a better balance between exploration and exploitations. Keywords: SGO, DE, Hybridization, Nature-inspired, Optimization algorithm

Convolutional Neural Network using Social Group Optimization for Electrocardiogram

Electrocardiogram (ECG) analysis plays a pivotal role in diagnosing various cardiac conditions, making it essential to develop accurate and robust classification models. This project proposes a novel approach to optimize ECG analysis by considering social group factors, leveraging the combined power of XG Boost classifier and Convolutional Neural Networks (CNNs). The methodology involves collecting a diverse dataset of ECG recordings, spanning various demographic groups. Preprocessing techniques are applied to standardize and clean the data, followed by feature extraction using CNNs to capture intricate patterns in the ECG signals. Subsequently, XG Boost classifier is employed to classify the ECG signals based on the extracted features, considering social group factors as additional input features. Keywords : Electrocardiogram (ECG), Convolutional Neural Network (CNN), XG Boost ,Social Group Optimization (SGO), Deep Learning ,Machine Learning , Signal Processing ,Classification ,Feature Extraction, Cardiac Diseases.

Estimation of Weibull Probability Distribution Parameters with Optimization Algorithms and Foça Wind Data Application

In this study, the scale and shape parameters of the Weibull probability distribution function (W.pdf) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foça region of İzmir in Turkey. The values of Weibull parameters obtained using Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Social Group Optimization (SGO), and Bat Algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (χ^2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foça were used. As a result of Foça speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and χ^2 criteria are evaluated together; it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power

Economic dispatch, emission dispatch, or their combination (EcD, EmD, EED) are essential issues in power systems optimization that focus on optimizing the efficient and sustainable use of energy resources to meet power demand. A new algorithm is proposed in this article to solve the dispatch problems with/without considering wind units. It is based on the Social Group Optimization (SGO) algorithm, but some features related to the selection and update of heuristics used to generate new solutions are changed. By applying the highly disruptive polynomial operator (HDP) and by generating sequences of random and chaotic numbers, the perturbation of the vectors composing the heuristics is achieved in our Modified Social Group Optimization (MSGO). Its effectiveness was investigated in 10-unit and 40-unit power systems, considering valve-point effects, transmission line losses, and inclusion of wind-based sources, implemented in four case studies. The results obtained for the 10-unit system indicate a very good MSGO performance, in terms of cost and emissions. The average cost reduction of MSGO compared to SGO is 368.1 $/h, 416.7 $/h, and 525.0 $/h for the 40-unit systems. The inclusion of wind units leads to 10% reduction in cost and 45% in emissions. Our modifications to MSGO lead to better convergence and higher-quality solutions than SGO or other competing algorithms.

Marine predators social group optimization: a hybrid approach

Marine predators social group optimization: a hybrid approach

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

Multi-objective social group optimization for machining process

Multi-objective social group optimization for machining process

Chaotic Social Group Optimization for Structural Engineering Design Problems

Chaotic Social Group Optimization for Structural Engineering Design Problems

Dual-Population Social Group Optimization Algorithm Based on Human Social Group Behavior Law

Inspired by the behavior law of human social groups, a new swarm intelligence algorithm named the dual-population social group optimization (DPSGO) algorithm is proposed in this article. Based on the primitive social group optimization (SGO) algorithm, dual-population grouping technology, reverse learning technology, immigration migration technology, and Gaussian mutation are introduced to further simulate the behavior law of actual human social groups. Experimental results and performance comparison show that the DPSGO algorithm has a better searchability and convergence rate. In addition, aiming at the socially hot issue of aviation safety, the simulation and experimental results show that the temperature measurement error can be reduced to less than 7.5 °C by using the DPSGO algorithm combined with reflected radiation correction to process the aeroengine multispectral radiation temperature measurement data. This article is of great significance to the design and optimization of swarm intelligence algorithms by using the behavior law of human social groups and provides valuable guidance for enhancing the safety monitoring of aeroengines.

Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application

Now-a-days, Segmentation is essential in diagnosing severe diseases wherever there is a scope for image processing. In this work, hybridization of most popular and metaheuristic algorithms with Conventional Neural Network (CNN) has been proposed. As a part of the study, jelly fish and Modified Social Group Optimization Algorithms (MSGOA) are used. The CNN weights and the corresponding hyper parameters are modified or designed with the help of the respective metaheuristic approach of the algorithm. This certainly improved the efficiency of the segmentation which is measured in several metrics of bio-medical image processing. The accuracy, loss, Intersection over Union (IoU) are some of those metrics which are employed in this study for better understanding of the algorithm’s effectiveness. Further the detection process is simulated consuming 100 iterations uniformly in either of the algorithms. The proposed methodology has efficiently segmented the tumor portion. The simulation has been carried out in MATLAB and the results are presented in terms of computed metrics, convergence plots and segmented images.

EACP: An effective automatic channel pruning for neural networks

The large data scale and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. However, channel pruning has become one of the most efficient methods for addressing this problem, with many existing researches proving its practicability in the field of model compression. The current channel pruning methods mainly start with the perspective of assessing the importance of channels or manual setting of the evaluation criteria, which requires unnecessary human intervention and shows the lack of certain automaticity. In this paper, an effective automatic channel pruning (EACP) method for neural networks is proposed. Specifically, we adopt the k-means++ method to cluster filters with similar features hierarchically in each convolutional layer, forming an initial compact compression structure. Subsequently, we use an improved social group optimization (SGO) algorithm to iteratively search and optimize the compression process of the post-clustered structure to find the optimal compressed structure. The effectiveness of the proposed approach is tested with respect to three leading CNN models on two image classification datasets. In CIFAR-10, our method reduces the FLOPs of GoogLeNet by 58.10 % and improves the accuracy by 0.20 % compared to the baseline.

Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm

Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm

Student psychology and social group optimizations based modified synchronous reference frame control topology for PV-DSTATCOM

This paper proposes the design of a modified synchronous reference frame (SRF) (MSRF) technique while incorporating student psychology based optimization (SPBO) and social group optimization (SGO) algorithms for solar photovoltaic (PV) and battery embedded distribution static compensator (DSTATCOM) for three-phase grid-tied system. For maximum power point tracking, the SPBO is used. The proposed MSRF controller provides satisfactory performance under different real-time cases. In case of night condition, the dc bus voltage is slightly reduced. MSRF may provide system stability under different faults. Due to the use of MSRF controller, the total harmonic distortion (THD) of grid current is 1.30% and the THD value is reduced by 9.72%, 23.98%, 26.14%, 66.23%, 36.59%, 38.97%, 56.38%, 30.85%, 41.44%, 75.05%, 77%, and 95.29%, respectively, as compared to the other techniques surfaced in the recent state-of-the-art literature such as (a) IT-2 fuzzy logic control (FLC), (b) type-I FLC, (c) PI control, (d) sliding mode control (SMC), (e) fuzzy and SMC, (f) indirect current control (ICC), (g) modified ICC, (h) SRF with teaching learning based optimization and SGO combination, (i) SRF with firefly and SGO combination, (j) SRF with particle swarm optimization and SGO combination, and (k) SRF control and without DSTATCOM.