The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as ...the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems.
•A balance adjustment mechanism by a weighting coefficient of acceleration is proposed.•A balance adjustment mechanism by a random weight of speed is proposed.•A balance adjustment mechanism by the new speed equation is proposed.•Advantages of the proposed algorithm is demonstrated by 30 benchmark functions in CEC 2014 and a real engineering problem.
•A new hybrid GA–GSA algorithm is proposed for FACTS based damping controller design.•The performance of the algorithm is tested with some standard bench-mark function.•Simulation results are ...presented and compared with other conventional techniques.
Tuning of damping controller parameters for optimal setting, such as gain and signal wash out block parameters, has major effects on its performance improvement. Estimation of optimum values for these parameters requires reliable and effective training methods so that the error during training reaches its minimum. This paper presents, a suitable tuning method for optimizing the damping controller parameters using a novel hybrid Genetic Algorithm–Gravitational Search Algorithm (hGA–GSA). The primary purpose is that the FACTS based damping controller parameter can be optimized using the proposed method. The central research objective here is that, how the system stability can be improved by the optimal settings of the variables of a damping controller obtained using the above proposed algorithm. Extensive experimental results on different benchmarks show that the hybrid algorithm performs better than standard gravitational search algorithm (GSA) and genetic algorithm (GA). In this proposed work, the comparison of the hGA–GSA algorithm with the GSA and GA algorithm in term of convergence rate and the computation time is carried out. The simulation results represent that the controller design using the proposed hGA–GSA provides better solutions as compared to other conventional methods.
•Estimation of candidate buses using Loss Sensitivity Factor, helps in reducing search space.•GSA to answer optimization problems with discontinuous solution space & objectives where global optimum ...is desired.•GSA is verified on reputed 33, 69, 85 & 141 Bus RDN.•Results attained make evident that GSA is superior to the techniques discussed in preceding literatures.
Power generated in generating station is transmitted through transmission lines and fed to the consumers through distribution substation. The power distributed into the network has losses, which is greater in distribution system compared to transmission system. This problem could be addressed by placing capacitor at strategic location due to which the kW loss can be minimized and the net savings can be maximized. This paper adopts two methods where the first method being the sensitivity analysis and the second method is the Gravitational Search Algorithm (GSA). Sensitivity analysis is a methodical technique, which is used to reduce the search space and to arrive at an accurate solution for recognizing the locality of capacitors. Capacitor values are allocated for the respective locations using GSA. The overall precision and dependability of the adopted approach were authenticated and verified on few radial distribution network with diverse topologies of varying sizes and complexities and also compared with an analytical Interior Point algorithm and one of the meta-heuristic optimization technique called Simulated Annealing. Computational outcomes obtained showed that the proposed method is capable of generating optimal solutions.
Gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the ...simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel GSA using quantum mechanics theories to generate a quantum-inspired gravitational search algorithm (QIGSA). The application of quantum mechanics theories in the proposed QIGSA provides a powerful strategy to diversify the algorithm’s population and improve its performance in preventing premature convergence to local optima. The simulation results and comparison with nine state-of-the-art algorithms confirm the effectiveness of the QIGSA in solving various benchmark optimization functions.
This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the ...Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.
Notice of Violation of IEEE Publication Principles "A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images" by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm," by Om Verma Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012
Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability ...for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning. Note to Practitioners -This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.
•An algorithm coded in C environment is developed to enhance the performance of the IREC’s microgrid system.•Local energy market cost model is proposed to obtain the best buying price in a day-ahead ...energy market.•Several real technical and market scenarios incorporating demand response, price sorting, etc., are considered in the study.•Simulation results demonstrate a significant reduction in the overall plant cost of the system.
Both performance optimization and scheduling of the distributed generation (DG) are relevant implementing an energy management system (EMS) within Microgrid (MG). Furthermore, optimization methods need to be applied to achieve maximum efficiency, improve economic dispatch as well as acquiring the best performance. This paper proposes an optimization method based on gravitational search algorithm to solve such problem in a MG including different types of DG units with particular attention to the technical constraints. This algorithm includes the implementation of some variation in load consumption model considering accessibility to the energy storage (ES) and demand response (DR). The proposed method is validated experimentally. Obtained results show the improved performance of the proposed algorithm in the isolated MG, in comparison with conventional EMS. Moreover, this algorithm which is feasible from computational viewpoint, has many advantages as peak consumption reduction, electricity generation cost minimization among other.
This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) ...unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.
Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data ...clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search Algorithm is proposed, which is called Bird Flock Gravitational Search Algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.
With the rapid growth of the number of Electric Vehicles (EVs), access to large-scale EVs will bring serious safety hazards to the operation planning of the power system. It needs to be supported by ...an effective EV charging and discharging behavior control strategy to meet the operation demand of the power system. An optimization model with the objectives of minimizing grid load variance and minimizing user charging cost is established. An improved hybrid algorithm is proposed for the optimal allocation of charging and discharging power of EVs by combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA). The performance of variant algorithm is tested using CEC2005 benchmarking functions sets and applied to the solution of the ordered charge–discharge optimal scheduling model. The results show that the convergence accuracy of the algorithm is better than the traditional algorithm, and it can effectively balance exploration and exploitation ability of the particles. In addition, the scheduling analysis is performed for different charging strategies of EVs. The scheduling results show that with the same optimization weights, implementing the ordered charging and discharging strategy can significantly reduce the charging cost of users and the load variance of the grid. Thus, the operational stability of the grid and the economic benefits for users are improved.
•Electric vehicle charging and discharging behavior control.•Improved hybrid algorithm for particle swarm optimization and gravitational search.•Reducing charging costs and smoothing grid load fluctuations.
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