Group Search Optimizer Research Articles

New robot path planning optimization using hybrid GWO-PSO algorithm

Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and follows path perfection guidelines, the primary shape is improved. In addition, simulation findings reveal that the proposed HGWO-PSO method is deeply serious in terms of path optimality when compared to path planning approaches such as group search optimizer GSO, PSO, artificial bee colony ABC, and GWO.

그룹탐색최적화를 적용한 트럭-드론 스케줄링

In parallel drone scheduling traveler salesman problem, the customer can be served by a single truck or by a fleet of one or more identical drones to minimize the completion time. The vehicle routes customer locations, while the drone fly back and forth between depot and customers. The objective of this paper is to propose novel Routing Group Search Optimization (RGSO) for truck-drone scheduling to find the best schedule and routing of a truck and a droneto better balance vehicle and drone delivery. The RGSO based on Group Search Optimization (GSO) is proposed for truck-drone system with scheduling solution representation. Our proposed RGSO is very effective in finding group which are generated by relative distances of customer locations for routing of truck. Our simulation results show that RGSO is very effective comparing to standard GSO based on the several experiments and analysis.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors.

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

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.

Development of adaptive AdaBoost classifier with optimal feature selection for enhanced intrusion detection in IoT

The fundamental intention of this paper is to model IDS in IoT platforms using an adaptive AdaBoost classifier. In the data collection phase, the intrusion dataset of IoT is collected from the standard benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data. The subsequent phase after data cleaning is optimal feature selection, in which the relevant features by eradicating the redundant information are extracted to minimise the classifier complexity. The main contribution of the proposed model is the development of an adaptive AdaBoost classifier, where the tuning of the number of estimators is done by the novel hybrid active electrolocation-based group search optimisation (AE-GSO). From the results, the accuracy of the AE-GSO-AdaBoost is 0.93%, 1.89%, 0.83%, and 1.25% advanced than AdaBoost, RNN, DBN, and DNN, respectively. Based on the comparative study of state-of-the-art works, the numerical outcomes describe the promising part of the suggested techniques, offering comparable detection accuracy to the traditional methods between the accuracy and other performance metrics.

Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk

The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

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.

Design and Implementation of GSO Based PI Controller for Non Linear Spherical Tank System

From literature point of view process parameter control for non linear process found to be issue in process control industries. The main dilemma lies on managing liquid level in globular tank which is non linear in nature owing to its area disparity in cross section consequent to the change in shape. System identification is made with black box model which is nonlinear and approximated to first order plus dead time model using the worst case model parameter criterion. Group Search Optimization based Proportional Integral controller is implemented to control the non linear system. The real time work is implemented in MATLAB using VMAT-01 module. The servo and regulatory responses of GSO based proportional integral controller intended for different operating region is noted and results are compared with the algorithms such as Big Bang - Big Crunch optimization, Particle swarm optimization and Bacterial Foraging optimization . The most admirable controlling method is determined based on indices such as reduced overshoot, better set point tracking, disturbance rejection , lower performance indices, faster settling time and rise time.

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.

An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering

Cluster analysis is one important field in pattern recognition and machine learning, consisting in an attempt to distribute a set of data patterns into groups, considering only the inner properties of those data. One of the most popular techniques for data clustering is the K-Means algorithm, due to its simplicity and easy implementation. But K-Means is strongly dependent on the initial point of the search, what may lead to suboptima (local optima) solutions. In the past few decades, Evolutionary Algorithms (EAs), like Group Search Optimization (GSO), have been adapted to the context of cluster analysis, given their global search capabilities and flexibility to deal with hard optimization problems. However, given their stochastic nature, EAs may be slower to converge in comparison to traditional clustering models (like K-Means). In this work, three hybrid memetic approaches between K-Means and GSO are presented, named FMKGSO, MKGSO and TMKGSO, in such a way that the global search capabilities of GSO are combined with the fast local search performances of K-Means. The degree of influence of K-Means on the behavior of GSO method is evaluated by a set of experiments considering both real-world problems and synthetic data sets, using five clustering metrics to access how good and robust the proposed hybrid memetic models are.

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.

Effect of Objective Function on the Optimization of Highway Vertical Alignment by Means of Metaheuristic Algorithms

The main purpose of this work is the comparison of several objective functions for optimization of the vertical alignment. To this end, after formulation of optimum vertical alignment problem based on different constraints, the objective function was considered as four forms including: 1) the sum of the absolute value of variance between the vertical alignment and the existing ground; 2) the sum of the absolute value of variance between the vertical alignment and the existing ground based on the diverse weights for cuts and fills; 3) the sum of cut and fill volumes; and 4) the earthwork cost and then the value of objective function was compared for the first three cases with the last one, which was the most accurate ones. In order to optimize the raised problem, Genetic Algorithm (GA) and Group Search Optimization (GSO) were implemented and performance of these two optimization algorithms were also compared. This research proves that the minimization of sum of the absolute value of variance between the vertical alignment and the existing ground, which is commonly used for design of vertical alignment, can’t at all grantee the optimum vertical alignment in terms of earthwork cost.

Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN

Intrusion detection has emerged as one of the major challenges involved in the cloud forensics. This work introduces an intrusion detection framework for the cloud environment with clustering and two-level classifiers. In the first step of the process, a Bayesian fuzzy clustering is used for clustering the nodes in the cloud. And in the next step, two-level gravitational group search-based support vector neural network (GG-SVNN) classifier identifies intrusion in clusters. GG-SVNN is a novel optimization scheme proposed in this work, by combining the group search optimizer, and gravitational search algorithm. The intrusion information provided by level 1 classifier is arranged to form compact data, and provided to the level 2 classifier. The level 2 classifier finally identifies total nodes affected by the intruders. The simulation of the proposed intrusion detection is done with the help of KDD cup dataset. From the simulation results, it is evident that the proposed GG-SVNN classifier has achieved overall best performance by achieving high accuracy value of 92.41% and low false alarm rate of 4.75% respectively.

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.

Maximising network lifetime and energy efficiency of wireless sensor network using group search Ant lion with Levy flight

Wireless sensor network (WSN) is one of the emerging techniques, providing pervasive computing for different implementations. Some of the renowned problems in WSN are energy consumption, as well as, the extension of network lifetime. Moreover, cluster head (CH) selection is one of the essential criteria since it has direct communication with the base station. Several researchers are in progress in designing the energy-efficient model. However, the problems are not yet rectified completely, which leads to the poor performance of WSN. This study intends to maximise the lifetime of the WSN by introducing an efficient CH selection algorithm. The CH selection is a crucial step that aids in improving the energy efficiency and network lifetime of a WSN. This study proposes a hybrid optimisation process named group search ant lion with Levy flight for choosing the CH in WSN. The proposed model is compared to the conventional models such as genetic algorithm, particle swarm optimisation, artificial bee colony, group search optimisation, ant lion optimisation and cuckoo search. The outcome of the simulation result shows the superiority of the proposed model by prolonging the lifetime of the network.