Group Search Optimization Algorithm Research Articles

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering

A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering

Two-stage diagnosis framework for heating pipeline system using improved group search optimizer

This paper presents a two-stage framework for monitoring the decline of pipeline insulation and diagnosing leakage fault. The task of the first stage is to estimate the parameters of the pipeline network, so as to monitor the abnormal heat conduction data of pipelines, and use the estimation results to update the parameters of the pipeline model. The task of the second stage is to obtain the location and the water loss of after a leakage fault. In the framework, the tasks of the two stages are formulated into the problem of minimizing the divergence between the real system and the simulation system. And the problem is solved by the group search optimizer (GSO) algorithm with improved boundary search strategy in this paper. In the simulation tests, the framework is verified to be able to diagnose the faults. And in comparison with other heuristic algorithms, the GSO algorithm has the best ability to accurately diagnose the fault. Finally, the framework is applied in the cooling system, which shows that the framework has good migration capability.

Hybrid support vector machine and K‐nearest neighbor‐based software testing for educational assistant

AbstractIn terms of training students for work in diverse firms, traditional and out‐of‐date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a Group Search Optimized two‐stage hybrid Support Vector Machine‐K‐Nearest Neighbor (SVM‐KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the group search optimization (GSO) algorithm is used to improve the parameter selection process of the two‐stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open‐source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.

Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics

This paper intends to propose a new model for detecting the patch based inpainting operation using Enhanced Deep Belief Network (E-DBN). The proposing model makes strong supervising of DBN that will capture the manipulated information. In fact, the enhancement is done under optimization concept, where the activation function and weight of DBN is optimally tuned by a new hybrid algorithm termed as Swarm Mutated Lion Algorithm (SM-LA). The hybridization model combines two conventional models: Group Search Optimizer (GSO) and Lion Algorithm (LA). Finally, the performance of proposed model is compared over other conventional models with respect to certain performance measures.

Retraction Note to: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

Retraction Note to: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

Probabilistic Load Shedding Scheme for Power System Voltage Stability Enhancement Using Discrete Group Search Optimization Algorithm

This paper presents a discrete heuristic algorithm to improve voltage stability margins of the power system, during under frequency conditions. Discrete group search optimization algorithm is proposed for the first time in this work and applied to a load shedding problem. Sometimes, after initial frequency drop following severe disturbances, the system frequency might return to its acceptable value, however, the system might become unstable due to voltage problems. This paper proposes discrete group search optimization algorithm to solve the load shedding problem as well as returning system frequency to the acceptable range. The objective of proposed method is to enhance voltage stability margins of the power system. Furthermore, in the paper the load uncertainty as the most critical source of uncertainty in power system is handled using point estimation approach. The proposed method is implemented on the modified IEEE 14-Bus test system as well as 39 bus New England Network to demonstrate its applicability and effectiveness. The performance of discrete group search optimization method is also compared with several methods in terms of enhancing voltage stability margins. The results confirm that the proposed method provides a considerable enhancement in the power system voltage stability margin.

Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization

The present paper aims to present a comprehensive multi-objective optimization model for energy management in local multi-energy systems in the presence of plug-in electric vehicles (PEVs), seeking to achieve the maximized profit of the operators of the local multi-energy systems and minimized CO2 emission at the same time. This problem involves technology transfer in local multi-energy systems and finding the PEVs charge/discharge optimization strategies in order to maximize the operators' profit and, in the meantime, reduce the CO2 emission. It can be solved by formulating a multi-purpose objective and can be dealt with by formulating a multi-objective programming problem through accurate modeling of mutual dependencies between the energy carriers. To solve this problem, a Modified Group Search Optimization (MGSO) algorithm is used based on the decomposition system. By the proposed structure, the local and global search is improved significantly. As indicated by the results of the effectiveness test of the optimization framework for maximizing the operator's profit and, meanwhile, reducing the CO2 emission, this objective is achievable through optimal coordination of multiple energy carriers in local multi-energy systems and effective management of the flexibility collected at both supply and demand sides.The simulation has been investigated in different scenarios. Obtained numerical analysis shows that the base case signifies the best case in terms of maximization of the local multi-energy systems operator’s profit through the optimized charging and discharging approaches of the PEVs. Also, in all scenarios, the profit of operator reductions by 5%–15% as compared with the base case. On the other hand, the best case scenario from an environmental point of view is provided by a scenario categorized with electric vehicles excluding under environmental optimization. In fact, CO2 emissions are reduced 7% compared to the emissions of base case.

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Chaotic binary Group Search Optimizer for feature selection

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm’s main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods’ performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing.

Clamp Nonlinear Modeling and Hysteresis Model Parameter Identification

Based on the Bouc-Wen model, a nonlinear hysteretic restoring force model is established with dynamic equations. The hardening and softening of the material after reaching the yield limit are described by the nonlinear restoring force-displacement hysteretic curve. The Gamultiobj algorithm and the group search optimization (GSO) algorithm are used to fit and identify the material softening and hardening hysteresis curves, respectively. The effectiveness of the optimization algorithm for identifying the parameters of the hysteretic model is verified, and the optimization effects of the two algorithms are compared. The results show that the Gamultiobj algorithm has better parameter identification ability and curve fitting ability for the hysteretic model. Then, the hysteresis curve describing the nonlinear characteristics of a clamp is obtained through the static stiffness experiment of the clamp. The experimental curve is fitted by the Gamultiobj algorithm. As a result, the nonlinear Bouc-Wen model parameters of the clamp are obtained, and the stiffness and damping of the clamp are recognized. The distribution statistics of the obtained parameters are performed, and it is found that each parameter satisfies a certain probability distribution, which indicates that the parameter identification result is reasonable.

Bio‐inspired hybrid algorithm to optimize pilot tone positions in polar‐code‐based orthogonal frequency‐division multiplexing–interleave division multiple access system

SummaryIn this paper, a hybrid bio‐inspired algorithm is proposed to enhance the performance of channel estimation of the orthogonal frequency‐division multiplexing–interleave division multiple access (OFDM–IDMA) system. This algorithm is a fusion of the lion algorithm and group search optimization algorithm named as SU‐LA, scrounger updated lion algorithm. It works on the optimal selection of pilot tone positions that ensure the attainment of the least value of mean square error. Polar code is used for error detection and correction along with Quadrature Amplitude Modulation (QAM) and 16 QAM techniques. Carrier frequency offset (CFO), user, and Eb/No factors are selected to analyze their impact on the BER performance in the presence of Rayleigh channel. It is observed that the proposed SU‐LA‐based system with the QAM technique achieves 91.23% enhancement in BER at zero CFO as compared to harmony search‐based system for Eb/No of 12 dB. Further, in the presence of 0.1 CFO, the proposed system retains BER performance. The experimentation is also carried with 16 QAM over various CFOs and the number of users. The BER performance of the proposed system is also superior to the particle swarm optimization (PSO) and genetic algorithm (GA)‐based system. The effect of peak‐to‐average power ratio (PAPR) and symbol time offsets is not considered.

Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty

Abstract Due to the spread and dispersed of load points and also growing penetration of wind farms to distribution systems, feeder reconfiguration strategy has been encountered with a high degree of uncertainty in such networks. In this way, efficient stochastic framework analysis based on cloud theory is proposed to handle the feeder reconfiguration problem considering uncertainty in load demands and wind turbine power. The cloud model, as a linguistic approach, comprises a correlation between randomness and fuzziness and provides good information to study the uncertain parameter effects on system performance through qualitative cloud models. According to qualitative-quantitative bidirectional transmission characteristic of the cloud theory through backward-forward cloud generator algorithm, a stochastic multi-objective feeder reconfiguration problem is formulated and solved utilizing powerful non-dominated sorting group search optimization algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using the fuzzy decision-making technique. To demonstrate the applicability of the proposed method and to compare obtained results with the other literature, deterministic and stochastic analysis is implemented on the IEEE 33-bus and 69-bus radial distribution systems. The superiority and satisfying performance of the proposed algorithm can be inferred from the quality of simulation solutions.

RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

In this paper, to keep the researchers interested in nature-inspired algorithms and optimization problems, a comprehensive survey of the group search optimizer (GSO) algorithm is introduced with detailed discussions. GSO is a nature-inspired optimization algorithm introduced by He et al. (IEEE Trans Evol Comput 13:973–990, 2009) to solve several different optimization problems. It is inspired by animal searching behavior in real life. This survey focuses on the applications of the GSO algorithm and its variants and results from the year of its suggestion (2009) to now (2020). GSO algorithm is used to discover the best solution over a set of candidate solution to solve any optimization problem by determining the minimum or maximum objective function for a specific problem. Meta-heuristic optimizations, nature-inspired algorithms, have become an interesting area because of their rule in solving various decision-making problems. The general procedures of the GSO algorithm are explained alongside with the algorithm variants such as basic versions, discrete versions, and modified versions. Moreover, the applications of the GSO algorithm are given in detail such as benchmark function, classification, networking, engineering, and other problems. Finally, according to the analyzed papers published in the literature by the all publishers such as IEEE, Elsevier, and Springer, the GSO algorithm is mostly used in solving various optimization problems. In addition, it got comparative and promising results compared to other similar published optimization algorithm.

Dominated GSO algorithm for optimal microgrid construction to improve consumer side properties in a distribution system

Introducing the concepts of distributed generation (DG) and microgrid (MG) are known as key points, which lead the conventional distribution systems toward novel smart grids. Utilizing DG infrastructures would facilitate local supply of loads. Hence, it is necessary to optimally distribute DG units all over the distribution network. MGs with ability of operating in the grid-connected and islanded modes enhance the system performance. Obviously, it is important to implement an appropriate procedure for optimal construction of MGs in a distribution system. This paper proposes optimized methods to allocate DG units and construct a set of MGs with the aim of improving system performance and consumer side properties. It is also taken into account that the generated power by DG units and the consumed power by loads are stochastic. The considered objective functions for optimal DG placement problem are decreasing system losses and improving voltage profile. On the other hand, MG construction is carried out with the aim of maximizing system load factor and minimizing system load variance. In this paper, an improved version of group search optimization (GSO) algorithm named as dominated groups search optimization (DGSO) algorithm is proposed and implemented in order to solve mentioned optimization problems. The proposed DGSO algorithm facilitates the comprehensive search which results in achieving to better solutions. The related formulation, optimization algorithms and simulation results are presented in this paper. Moreover, the proposed strategy is evaluated in the PG&E 69-bus distribution system and a custom meshed distribution network.

RETRACTED ARTICLE: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

Managing non functional requirements has been a challenge onto software engineers for a long time. Throughout the years, numerous strategies and methods have been proposed to enhance their elicitation, documentation and approval. Similarly, security requirements emphatically impact the building configuration of complex IT frameworks comparatively as non-functional requirements. Both security building and software engineering furnish strategies to manage such necessities. Nonetheless, there is still a basic crevice concerning the joining of techniques for these two different fields. In this paper we close this crevice as for security prerequisites by proposing a technique that joins software engineering technologies with the best in class security engineering standards. An efficient quality requirement based software model with security requirements is developed by fusing Group Search Optimization and genetic algorithm in our proposed methodology.

A two-stage Microgrid cost optimization considering distribution network loss and voltage deviation

This paper proposes a novel two-stage Microgrid (MG) scheduling methodology to decide optimal location and size of MG and outputs of dispatchable and intermittent distributed generators (DGs). In the first stage, a system stability index (SSI) representing the performance of the distribution network is proposed to select the location of the MG. The size of the MG is derived by minimizing of SSI. In the second stage, the group search optimizer (GSO) algorithm is applied to further determine the outputs of each DG in the MG, including photovoltaic (PV) panels, wind turbines (WT), battery energy storage systems (BESS) and diesel generators (DIG). To validate the proposed approach, the IEEE 33-bus radial distribution systems has been applied. The simulation results show that the approach minimizes MG investment costs under the premise of considering loss and voltage deviation of the distribution network.

A weighted centroid correction method for wireless sensor network based on GSO algorithm

To overcome the ambiguity of the location information of wireless sensor network nodes and lead to the low accuracy of weighted centroid localisation results, a new weighted centroid correction method based on group search optimiser (GSO) algorithm for wireless sensor networks is proposed in this paper. This method randomly drops sensor nodes into the area to be monitored to form a wireless sensor network. The experimental results show that the network coverage is close to 100%, the energy consumption per unit during receiving and transmitting accounts for 0.31% of the total battery. The absolute positioning error of each node is 6-8.5 m, which can achieve the expected goal of this study.

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naive Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.

A Trust based Multipath Routing for Black Hole Attacks with Group Search Optimization Routing

The Mobile Ad hoc Network (MANET) is the collection of some mobile devices that communicate with one another without any help from a centralized administration. For this, the energy is an important issue that has to be addressed since the ad-hoc network nodes have a limited battery power. A secure routing in the MANETs is an area of research that is emerging. The designing of a security protocol which is trustworthy for the ad hoc routing is an extremely challenging task owing to all its unique traits. Due to its minimal configuration and its quick deployment, the MANETs are found to be well-suited for situations in the case of an emergency such as the natural disasters or the military applications. This way, a transfer of data among two nodes need some security. A MANET black-hole attack will occur owing to the malicious nodes attracting data packets by means of a false advertisement of a fresh route to its destination. For this work, the trust-based routing along with the packet forwarding probability. For this work, the Ad hoc On-Demand Multipath Distance Vector Routing (AOMDV) protocol is used and for finding the routing path a trust value will be piggybacked with a route request packet. There was yet another novel algorithm for optimization known as the Group Search Optimizer (GSO) algorithm that was proposed inspired by the behaviour of animals. This GSO-AOMDV was for the purpose of improving the performance of the network. The results of the experiment proved that this method proposed could achieve a better performance compared to the other method.