Producer pre-selection mechanism based on self-adaptive group search optimizer

An improved group search optimizer with operation of quantum-behaved swarm and its application

In this paper, a new optimization algorithm is presented for coordinated design of a conventional power system stabilizer (CPSS) and a unified power flow controller (UPFC) that are connected to a single-machine infinite-bus (SMIB) power system. First, this coordinated design problem is converted into an optimization problem. Then, the group search optimization (GSO) algorithm is used to solve it. The proposed algorithm is tested under various operating conditions and its effectiveness is proved by simulation results.

Accurate damping of inter-area and intra area oscillations are very much critical in the stability of electric power system. Power system stabilizer (PSS) is mainly used as auxiliary controllers to damp out the low frequency oscillations in the area of electric power system. The tuning of Conventional Power System Stabilizer (CPSS) for a nonlinear power system should be robust such that overall system stability can be improved under several operating points. Today's scenario, various populations based heuristic algorithms are available to minimize the effect of stability problem by properly tuning the parameters of CPSS. In this paper presents a new heuristic population based optimization technique named as Group Search Optimization (GSO) algorithm to tune the PSS parameters for a multi-machine system for different operating conditions. The importance of the GSO algorithm is evaluated and compared for damping the system oscillations during small and large disturbances, and their robustness is illustrated using the transient signal analysis.

Chapter 1 Introduction of swarm intelligent algorithms.- Chapter 2 Application of particle swarm optimization algorithm to engineering structures.- Chapter 3 Optimum design of structures with heuristic particle swarm optimization algorithm .-Chapter 4 Optimum design of structures with group search optimizer algorithm .-Chapter 5 Improvements and applications of group search optimizer in structural optimal design .-Chapter 6 Optimum design of structures with quick group search optimization algorithm .-Chapter 7 Group search optimizer and its applications on multi-objective structural optimal design .-Chapter 8 Prospecting swarm intelligent algorithms.

In 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.

This article presents a scheme using singular value decomposition (SVD) combined group search optimizer (GSO) algorithm to estimate the parameters of low frequency oscillation (LFO) in power grids. Firstly, Mathematical morphology (MM) is adopted as a preprocessing method. Secondly, SVD is applied to identify the number of modes in a LFO event. Finally, parameters of each mode are identified and determined by GSO. In order to demonstrate the accuracy and efficiency of the scheme proposed in this article, three simulation cases are implemented, which use predefined parameters, real-time digital system and wide-area measurement signal data collected from North American SynchroPhasor Initiative, respectively. The analysis results indicate that the proposed scheme can be applied in real-time monitoring environment on digital signal processor platform within short computation time, even in heavy noisy environment.

Watermarking is defined as about hiding any valuable information set in the particular digital media by itself through modifying the particular graphic contents. In our proposed method, the process of watermarking is performed from video data. In this work group search optimization is used to determine the position, selection for watermarking. The proposed framework includes the following stages particularly, frame conversion, preprocessing, watermark image selection, watermarking position selection and inverse watermarking. Initially, the input video is segregated into a number of frames. Then in preprocessing, both the input and the original image are resized and the original image is transformed by discrete wavelet transform. Then the secret data to be embedded is selected and replaced in the watermark position determined through the Group Search Optimization (GSO) Algorithm. Finally, inverse watermarking is performed to retrieve the secret data. The proposed technique is implemented in the working platform of MATLAB which shows improved results by using GSO as compared to without GSO method.

Group search optimizer (GSO) which is an effective evolutionary algorithm has been successfully applied in many applications. However, the purely stochastic resampling or selection mechanism in GSO leads to long computing time and premature convergence. In this paper, we propose a diversity-guided group search optimizer (DGSO) with opposition-based learning (OBL) to overcome these limitations. Opposition-based learning is utilized to accelerate the convergence rate of GSO, while the diversity guidance (DG) is used to increase the diversity of population. When compared with the standard GSO, a novel operator using OBL and DG is developed for the population initialization as well as the generation jumping. A comprehensive set of 19 complex benchmark functions is used for experiment verification and is compared to the original GSO algorithm. Numerical experiments show that the proposed DGSO leads to better performance in comparison with the standard GSO in the convergence rate and the solution accuracy.

We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach.

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

Quasi-oppositional group search optimization for multi-area dynamic economic dispatch

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.

In this paper, we propose a novel optimization algorithm, Group Search Optimizer (GSO), which is inspired by animal searching behaviour and group living theory. The algorithm is based on the Producer-Scrounger model [1], which assumes group members search either for ‘ finding’ (producer) or for ‘ joining’ (scrounger) opportunities. Animal scanning mechanisms (e.g., vision) are incorporated to develop the algorithm. We also employ ‘ rangers’ which perform random walks to avoid entrapment in local minima. When tested against benchmark functions, GSO outperformed competitively with other evolutionary algorithms in terms of accuracy and convergence speed on most of the benchmark functions.

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