Статті в журналах: "GRAVITATIONAL SEARCH ALGORITHM (GSA)"

1

Lenin, K. "MINIMIZATION OF REAL POWER LOSS BY ENHANCED GRAVITATIONAL SEARCH ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 7 (July 31, 2017): 623–30. http://dx.doi.org/10.29121/granthaalayah.v5.i7.2017.2171.

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In this paper, Enhanced Gravitational Search (EGS) algorithm is proposed to solve the reactive power problem. Gravitational search algorithm (GSA) results are improved by using artificial bee colony algorithm (ABC). In GSA, solutions are fascinated towards each other by applying gravitational forces, which depending on the masses assigned to the solutions, to each other. The heaviest mass will move slower than other masses and pull others. Due to nature of gravitation, GSA may pass global minimum if some solutions stuck to local minimum. ABC updates the positions of the best solutions that have obtained from GSA, preventing the GSA from sticking to the local minimum by its strong penetrating capability. The proposed algorithm improves the performance of GSA in greater level. In order to evaluate the performance of the proposed EGS algorithm, it has been tested on IEEE 57,118 bus systems and compared to other standard algorithms.

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Lenin, K. "A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 11 (November 30, 2017): 316–24. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2358.

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This paper proposes Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS) for solving optimal reactive power problem. Individual position modernize strategy in Gravitational Search Algorithm (GSA) may cause damage to the individual position and also the local search capability of GSA is very weak. The new HGS algorithm introduced the idea of Simulated Annealing (SA) into Gravitational Search Algorithm (GSA), which took the Metropolis-principle-based individual position modernize strategy to perk up the particle moves, & after the operation of gravitation, Simulated Annealing operation has been applied to the optimal individual. In order to evaluate the efficiency of the proposed Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS), it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to the standard reported algorithms. Simulation results show that HGS is superior to other algorithms in reducing the real power loss and voltage profiles also within the limits.

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3

Rashedi, Esmat, Hossein Nezamabadi-pour, and Saeid Saryazdi. "GSA: A Gravitational Search Algorithm." Information Sciences 179, no. 13 (June 2009): 2232–48. http://dx.doi.org/10.1016/j.ins.2009.03.004.

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4

Shankar, Rajendran, Narayanan Ganesh, Robert Čep, Rama Chandran Narayanan, Subham Pal, and Kanak Kalita. "Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization." Processes 10, no. 3 (March 21, 2022): 616. http://dx.doi.org/10.3390/pr10030616.

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The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.

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Kamaruzaman, Anis Farhan, Azlan Mohd Zain, Suhaila Mohamed Yusuf, and Noordin Mohd Yusof. "Gravitational Search Algorithm for Engineering: A Review." Applied Mechanics and Materials 815 (November 2015): 417–20. http://dx.doi.org/10.4028/www.scientific.net/amm.815.417.

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This paper presents a review on gravitational search algorithm (GSA). Nowadays, GSA has been used in various engineering studies such as production cost, production time, power consumption and emission. The GSA also mainly focuses to solve the problem related to optimization, modeling, scheduling and clustering. This paper also highlights the current researches using improved GSA.

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Siddique, Nazmul, and Hojjat Adeli. "Gravitational Search Algorithm and Its Variants." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1639001. http://dx.doi.org/10.1142/s0218001416390018.

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best fitness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a specified number of iterations after which the best fitness becomes the global fitness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.

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Kherabadi, Hossein Azadi, Sepehr Ebrahimi Mood, and Mohammad Masoud Javidi. "Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller." Cybernetics and Information Technologies 17, no. 1 (March 1, 2017): 72–86. http://dx.doi.org/10.1515/cait-2017-0006.

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Abstract Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

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Ali, Ahmed F., and Mohamed A. Tawhid. "Direct Gravitational Search Algorithm for Global Optimisation Problems." East Asian Journal on Applied Mathematics 6, no. 3 (July 20, 2016): 290–313. http://dx.doi.org/10.4208/eajam.030915.210416a.

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AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.

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SIDDIQUE, Nazmul, and Hojjat ADELI. "APPLICATIONS OF GRAVITATIONAL SEARCH ALGORITHM IN ENGINEERING." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 22, no. 8 (November 25, 2016): 981–90. http://dx.doi.org/10.3846/13923730.2016.1232306.

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems.

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Santra, D., A. Mukherjee, K. Sarker, and S. Mondal. "Hybrid Genetic Algorithm-Gravitational Search Algorithm to Optimize Multi-Scale Load Dispatch." International Journal of Applied Metaheuristic Computing 12, no. 3 (July 2021): 28–53. http://dx.doi.org/10.4018/ijamc.2021070102.

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Genetic algorithm (GA) and gravitational search algorithm (GSA) both have successfully been applied in solving ELD problems of electrical power generation systems. Each of these algorithms has their limitations and advantage. GA's global search and GSA's local search capability are their strong points while long execution period of GA and premature of convergence of GSA hinders the possibility of optimum result when applied separately in ELD problems. To mitigate these limitations, experiment is done for the first time by combining GA and GSA suitably and applying the hybrid in non-linear ELD problems of 6, 15, and 40 unit test systems. The paper reports the details of this study including comparative analysis considering similar hybrid algorithms. The result strongly attests the quality, consistency, and overall effectiveness of the GA-GSA hybrid in ELD problems.

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11

Koay, Ying-Ying, Jian-Ding Tan, Chin-Wai Lim, Siaw-Paw Koh, Sieh-Kiong Tiong, and Kharudin Ali. "An adaptive gravitational search algorithm for global optimization." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 724. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp724-729.

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<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

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12

Can, Umit, and Bilal Alatas. "Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm." International Journal of Software Engineering and Knowledge Engineering 27, no. 03 (April 2017): 343–72. http://dx.doi.org/10.1142/s0218194017500127.

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The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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13

Lin, Mingmin, Yingpei Zeng, Ting Wu, Qiuhua Wang, Linan Fang, and Shanqing Guo. "GSA-Fuzz: Optimize Seed Mutation with Gravitational Search Algorithm." Security and Communication Networks 2022 (July 15, 2022): 1–17. http://dx.doi.org/10.1155/2022/1505842.

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Mutation-based fuzzing is currently one of the most effective techniques to discover software vulnerabilities. It relies on mutation strategies to generate interesting seeds. As a state-of-the-art mutation-based fuzzer, AFL follows a mutation strategy with high randomization, which uses randomly selected mutation operators to mutate seeds at random offsets. Its strategy may ignore some efficient mutation operators and mutation positions. Therefore, in this paper, we propose a solution named GSA-Fuzz to improve the efficiency of seed mutation strategy with the gravitational search algorithm (GSA). GSA-Fuzz uses GSA to learn the optimal selection probability distributions of operators and mutation positions and designs a position-sensitive strategy to guide seed mutation with learned distributions. Besides, GSA-Fuzz also provides a flip mode to calculate the efficiencies of the deterministic stage and indeterministic stage and implements switching between the two stages to further improve the efficiency of seed mutation. We compare GSA-Fuzz with the state-of-the-art fuzzers AFL, MOPT-AFL, and EcoFuzz on 10 open-source programs. GSA-Fuzz finds 145% more paths than AFL, 66% more paths than EcoFuzz, and 43% more paths than MOPT-AFL. In addition, GSA-Fuzz also outperforms other fuzzers in bug detection and line coverage.

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14

Purwoharjono, Purwoharjono Purwoharjono. "Penerapan Metode Gravitational Search Algorithm Menggunakan Static VAR Compensator." Jurnal Sistem dan Teknologi Informasi (JustIN) 10, no. 1 (January 31, 2022): 175. http://dx.doi.org/10.26418/justin.v10i1.50575.

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Penerapan metode Gravitational Search Algorithm (GSA) ini bertujuan memperbaiki profil tegangan tenaga listrik menggunakan Static VAR Compensator (SVC). Penelitian ini dibandingkan hasil simulasi sebelum pemasangan SVC menggunakan metode Newton Raphson (NR) dan sesudah pemasangan SVC menggunakan metode GSA. Lokasi implementasi penelitian ini adalah system kelistrikan Jawa-Bali 500 kV. Hasil simulasi sesudah pemasangan SVC menggunakan metode GSA ini lebih baik dibandingkan dengan hasil simulasi sebelum pemasangan SVC menggunakan metode NR. Hasil simulasi sesudah pemasangan SVC menggunakan metode GSA ini juga, dapat memperbaiki profil tegangan pada system Jawa-Bali 500 kV.

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15

Nobahari, Hadi, Mahdi Nikusokhan, and Patrick Siarry. "A Multi-Objective Gravitational Search Algorithm Based on Non-Dominated Sorting." International Journal of Swarm Intelligence Research 3, no. 3 (July 2012): 32–49. http://dx.doi.org/10.4018/jsir.2012070103.

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This paper proposes an extension of the Gravitational Search Algorithm (GSA) to multi-objective optimization problems. The new algorithm, called Non-dominated Sorting GSA (NSGSA), utilizes the non-dominated sorting concept to update the gravitational acceleration of the particles. An external archive is also used to store the Pareto optimal solutions and to provide some elitism. It also guides the search toward the non-crowding and the extreme regions of the Pareto front. A new criterion is proposed to update the external archive and two new mutation operators are also proposed to promote the diversity within the swarm. Numerical results show that NSGSA can obtain comparable and even better performances as compared to the previous multi-objective variant of GSA and some other multi-objective optimization algorithms.

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Mutlag, Ammar Hussein, Omar Nameer Mohammed Salim, and Siraj Qays Mahdi. "Optimum PID controller for airplane wing tires based on gravitational search algorithm." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 1905–13. http://dx.doi.org/10.11591/eei.v10i4.2953.

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In this paper, the gravitational search algorithm (GSA) is proposed as a method for controlling the opening and closing of airplane wing tires. The GSA is used to find the optimum proportional-integral-derivative (PID) controller, which controls the wing tires during take-off and landing. In addition, the GSA is suggested as an approach for overcoming the absence of the transfer function, which is usually required to design the optimum PID. The use of the GSA is expected to improve the system. Two of the most popular optimisation algorithms-the harmony search algorithm (HSA) and the particle swarm optimisation (PSO)-were used for the sake of comparison. Moreover, the GSA-, HSA- and PSO-based optimum PID controllers were compared with one of the most important PID tuning methods, the Ziegler-Nichols (ZN) method. In this study, the integral time absolute error (ITAE) was used as a fitness function. First, four transfer functions for different applications were used to compare the performance of the GSA-based PID (PID-GSA), HSA-based PID (PID-HSA), PSO-based PID (PID-PSO) and Ziegler-Nichols-based PID (PID-ZN). Next, the GSA was used to design the optimum PID controller for the opening and closing systems of the airplane wing tires. The results reveal that the GSA provides better outcomes in terms of ITAE when compared with the other adopted algorithms. Furthermore, the GSA demonstrates a fast and robust response to reference variation.

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17

Hu, Hongping, Xiaxia Cui, and Yanping Bai. "Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm." Advances in Mathematical Physics 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/2131862.

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Gravitational Search Algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO-GSA) and propose an improved PSO-GSA algorithm (written as I-PSO-GSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I-PSO-GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO-GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, I-PSO-GSA outperforms PSO-GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

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Wang, Tongxiang, Xianglin Wei, Jianhua Fan, and Tao Liang. "Jammer Localization in Multihop Wireless Networks Based on Gravitational Search." Security and Communication Networks 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/7670939.

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Multihop Wireless Networks (MHWNs) can be easily attacked by the jammer for their shared nature and open access to the wireless medium. The jamming attack may prevent the normal communication through occupying the same wireless channel of legal nodes. It is critical to locate the jammer accurately, which may provide necessary message for the implementation of antijamming mechanisms. However, current range-free methods are sensitive to the distribution of nodes and parameters of the jammer. In order to improve the localization accuracy, this article proposes a jammer localization method based on Gravitational Search Algorithm (GSA), which is a heuristic optimization evolutionary algorithm based on Newton’s law of universal gravitation and mass interactions. At first, the initial particles are selected randomly from the jammed area. Then, the fitness function is designed based on range-free method. At each iteration, the mass and position of the particles are updated. Finally, the position of particle with the maximum mass is considered as the estimated jammer’s position. A series of simulations are conducted to evaluate our proposed algorithms and the simulation results show that the GSA-based localization algorithm outperforms many state-of-the-art algorithms.

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Wu, Yi, Qiu Hua Tang, Li Ping Zhang, Zi Xiang Li, and Xiao Jun Cao. "Solving Two–Sided Assembly Line Balancing Problem via Gravitational Search Algorithm." Applied Mechanics and Materials 697 (November 2014): 450–55. http://dx.doi.org/10.4028/www.scientific.net/amm.697.450.

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Two-sided assembly lines are widely applied in plants for producing large-sized high volume products, such as trucks and buses. Since the two-sided assembly line balancing problem (TALBP) is NP-hard, it is difficult to get an optimal solution in polynomial time. Therefore, a novel swarm based heuristic algorithm named gravitational search algorithm (GSA) is proposed to solve this problem with the objective of minimizing the number of mated-stations and the number of stations simultaneously. In order to apply GSA to solving the TALBP, an encoding scheme based on the random-keys method is used to convert the continuous positions of the GSA into the discrete task sequence. In addition, a new decoding scheme is implemented to decrease the idle time related to sequence-dependent finish time of tasks. The corresponding experiment results demonstrate that the proposed algorithm outperforms other well-known algorithms.

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Yu, Xiaobing, Xianrui Yu, and Xueying Zhang. "Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11007–22. http://dx.doi.org/10.3233/jifs-202132.

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Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.

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Falah, Miftahul, Dian Palupi Rini, and Iwan Pahendra. "Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (January 22, 2021): 90. http://dx.doi.org/10.30865/mib.v5i1.2597.

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Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.

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Rather, Sajad Ahmad, and P. Shanthi Bala. "Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems." World Journal of Engineering 17, no. 1 (February 5, 2020): 97–114. http://dx.doi.org/10.1108/wje-09-2019-0254.

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Purpose The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD). Design/methodology/approach In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA. Findings The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms. Research limitations/implications The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences. Originality/value The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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Albatran, Saher, Muwaffaq I. Alomoush, and Ahmed M. Koran. "Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 780. http://dx.doi.org/10.11591/ijece.v8i2.pp780-792.

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Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

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Tian, Mengnan, Junhua Liu, Wei Yue, and Jie Zhou. "A Novel Integrated Heuristic Optimizer Using a Water Cycle Algorithm and Gravitational Search Algorithm for Optimization Problems." Mathematics 11, no. 8 (April 15, 2023): 1880. http://dx.doi.org/10.3390/math11081880.

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This paper presents a novel composite heuristic algorithm for global optimization by organically integrating the merits of a water cycle algorithm (WCA) and gravitational search algorithm (GSA). To effectively reinforce the exploration and exploitation of algorithms and reasonably achieve their balance, a modified WCA is first put forward to strengthen its search performance by introducing the concept of the basin, where the position of the solution is also considered into the assignment of the sea or river and its streams, and the number of the guider solutions is adaptively reduced during the search process. Furthermore, the enhanced WCA is adaptively cooperated with the gravitational search to search for new solutions based on their historical performance within a certain stage. Moreover, the binomial crossover operation is also incorporated after the water cycle search or the gravitational search to further improve the search capability of the algorithm. Finally, the performance of the proposed algorithm is evaluated by comparing with six excellent meta-heuristic algorithms on the IEEE CEC2014 test suite, and the numerical results indicate that the proposed algorithm is very competitive.

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Rather, Sajad Ahmad, and P. Shanthi Bala. "Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization." International Journal of Applied Metaheuristic Computing 13, no. 1 (January 2022): 1–57. http://dx.doi.org/10.4018/ijamc.292496.

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The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

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Pramana, Setia, and Imam Habib Pamungkas. "Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (February 28, 2018): 10. http://dx.doi.org/10.21609/jiki.v11i1.580.

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Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.

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Acherjee, Bappa, Debanjan Maity, and Arunanshu S. Kuar. "Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms." International Journal of Applied Metaheuristic Computing 11, no. 2 (April 2020): 1–26. http://dx.doi.org/10.4018/ijamc.2020040101.

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The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravitational search algorithms (GSAs), genetic algorithms (GAs), particle swarm optimization (PSO) algorithms, ant colony optimization (ACO) algorithms and artificial bee colony (ABC) algorithms, and gives exactly the same results as predicted by the fireworks algorithm (FWA). The CS algorithm outperforms all other algorithms namely CSO, FWA, GSA, GA, PSO, ACO, and ABC algorithms in terms of mean computational time, whereas, the CSO algorithm outperforms all other algorithms except for the CS and GSA algorithms.

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Sarjila, R., K. Ravi, J. Belwin Edward, K. Sathish Kumar, and Avagaddi Prasad. "Parameter Extraction of Solar Photovoltaic Modules Using Gravitational Search Algorithm." Journal of Electrical and Computer Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2143572.

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Parameter extraction of a solar photovoltaic system is a nonlinear problem. Many optimization algorithms are implemented for this purpose, which failed in giving better results at low irradiance levels. This article presents a novel method for parameter extraction using gravitational search algorithm. The proposed method evaluates the parameters of different PV panels at various irradiance levels. A critical evaluation and comparison of gravitational search algorithm with other optimization techniques such as genetic algorithm are given. Extensive simulation analyses are carried out on the proposed method and show that GSA is much suitable for parameter extraction problem.

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Jiang, Shanhe, Chaolong Zhang, and Shijun Chen. "Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients." Mathematical Problems in Engineering 2020 (April 21, 2020): 1–17. http://dx.doi.org/10.1155/2020/1957812.

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Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

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Sidhu, D. S., J. S. Dhillon, and Dalvir Kaur. "Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Gravitational Search Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/282809.

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In the recent years, the digital IIR filter design as a single objective optimization problem using evolutionary algorithms has gained much attention. In this paper, the digital IIR filter design is treated as a multiobjective problem by minimizing the magnitude response error, linear phase response error and optimal order simultaneously along with meeting the stability criterion. Hybrid gravitational search algorithm (HGSA) has been applied to design the digital IIR filter. GSA technique is hybridized with binary successive approximation (BSA) based evolutionary search method for exploring the search space locally. The relative performance of GSA and hybrid GSA has been evaluated by applying these techniques to standard mathematical test functions. The above proposed hybrid search techniques have been applied effectively to solve the multiparameter and multiobjective optimization problem of low-pass (LP), high-pass (HP), band-pass (BP), and band-stop (BS) digital IIR filter design. The obtained results reveal that the proposed technique performs better than other algorithms applied by other researchers for the design of digital IIR filter with conflicting objectives.

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Amer, Noor Hafizah, Nurhidayati Ahmad, and Amar Faiz Zainal Abidin. "Weight Minimization of Helical Compression Spring Using Gravitational Search Algorithm (GSA)." Applied Mechanics and Materials 773-774 (July 2015): 277–81. http://dx.doi.org/10.4028/www.scientific.net/amm.773-774.277.

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Compression spring is one of the most common mechanical componet being used in most mechanisms. Many criteria and constraints should be considered in designing and specifying the spring dimensions. Therefore, it has been one of the standard case studies considered to test a new optimisation algorithm. This paper introduced an optimization method named Gravitational search Algorithm (GSA) to solve the problem of weight minimization of spring. From previous studies, weight minimization of a spring has been investigated by many researcher using various optimization algorithm technique. The result of this study were compared to one of the previous studies using Particle Swarm Optimization (PSO) algorithm. Also, parametric studies were conducted to select the best values of GSA parameters, beta and epsilon. From the results obtained, it was observed that the optimum dimensions and weight obtained by GSA are better than the values obtained by PSO. The best values of beta and epsilon was found to be 0.6 and 0.01 respectively.

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32

Worasucheep, Chukiat. "Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (March 18, 2019): 176–86. http://dx.doi.org/10.37936/ecti-cit.2018122.134980.

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Gravitational Search Algorithm (GSA) is a recent stochastic search algorithm that is inspired from the concepts of gravity rule and law of motion in physics. Despite its success and attractiveness, it has some coefficients and parameters that should be properly tuned to improve its performance. This paper studies the performance of GSA by varying the parameters that controls its gravitational force. Then a new differential mutation operator is proposed to enhance performance of GSA by accelerating its convergence. The proposed algorithm, namely DMGSA, is evaluated using 15 well-known benchmark functions from the special session of CEC2013 with different characteristics including randomly shifted optimum, rotation and non-separability. The results obviously confirms the performance achieved from the proposed mutation operator outperforms that from the attempts of parameter tuning in the original GSA. Lastly, DMGSA is applied for optimizing a small-scale gene regulatory network. The result demonstrates that its performance is highly competitive and clearly surpasses original GSA.

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Hota, Prakash Kumar, and Nakul Charan Sahu. "Non-Convex Economic Dispatch with Prohibited Operating Zones through Gravitational Search Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (December 1, 2015): 1234. http://dx.doi.org/10.11591/ijece.v5i6.pp1234-1244.

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This paper presents a new approach to the solution of optimal power generation for economic load dispatch (ELD) using gravitational search algorithm (GSA) when all the generators include valve point effects and some/all of the generators have prohibited operating zones. In this paper a gravitational search algorithm is suggested that deals with equality and inequality constraints in ELD problems. A constraint treatment mechanism is also discussed to accelerate the optimization process<strong>. </strong>To verify the robustness and superiority of the proposed GSA based approach, a practical sized 40-generators case with valve point effects and prohibited operating zones is considered. The simulation results reveal that the proposed GSA approach ensures convergence within an acceptable execution time and provides highly optimal solution as compared to the results obtained from well established heuristic optimization approaches.

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34

Jiang, Shanhe, Chaolong Zhang, Wenjin Wu, and Shijun Chen. "Combined Economic and Emission Dispatch Problem of Wind-Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2019 (November 21, 2019): 1–19. http://dx.doi.org/10.1155/2019/5679361.

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In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind-thermal power system. The proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle’s velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull-based probability density function (PDF) is utilized. The CEED model employed consists of the fuel cost objective and emission-level target produced by conventional thermal generators and the operational cost generated by wind turbines. The effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind-thermal power system. Results of GPSOA-based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state-of-the-art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.

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Zhang, Daming, Fangjin Sun, Tiantian Liu, and Zhonghao Xu. "Mixture Ratio Design Optimization of Coal Gangue-Based Geopolymer Concrete Based on Modified Gravitational Search Algorithm." Advances in Civil Engineering 2021 (April 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/6620853.

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A green concrete, new type of coal gangue-based geopolymer concrete, was prepared. Coal gangue geopolymer concrete contains many mineral admixtures and alkaline activators; the concrete mixture ratio design has always been a complex problem. The framework of the mix design optimization by the proposed method is established in this work. The paper aims to minimize the economic cost under the premise of ensuring the strength and workability of coal gangue-based geopolymer concrete. Gravitational search algorithm (GSA) has the advantages of faster convergence speed and stronger exploitation performance compared with the traditional optimization algorithms. However, GSA tends to premature convergence and local optimum, with weak search ability. Therefore, chaotic map is introduced in the work here. Gravitational search algorithm was modified based on Chebyshev map in chaotic theory, and the modified equations were derived. The modified algorithm was verified by the calculation of typical functions. And results from traditional GSA and GSA modified by another chaotic mapping, logistic mapping, were compared and the characteristics of different GSA were analyzed and concluded. After that, the mix design of geopolymer concrete based on coal gangue with different strength grades was optimized with the modified GSA. Through analysis of the optimization results, cost variation of different strength grade coal gangue-based geopolymer concrete was revealed. Costs declined significantly; the higher the grades within a certain strength range, the more saved. Therefore, it can be inferred that the modified gravity search method provides a reliable tool for the optimization of mixture ratio of similar geopolymer concrete.

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36

Haeri, Ali, and Mohammad Javad Fadaee. "The Gravitational Search Algorithm in Antiresonance Layer Optimization of Laminated Composite Plates." International Journal of Computational Methods 14, no. 06 (August 2017): 1750070. http://dx.doi.org/10.1142/s0219876217500700.

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In the present study, Gravitational Search Algorithm (GSA) is combined with Finite Element Method (FEM) for optimizing laminated composites vibration behavior. The fiber orientation angle of layers is considered as design variable. The 8-layerd and 12-layerd plates with both of square and rectangular shapes are investigated. Twenty distinct boundary conditions and [Formula: see text] of fiber angle increment are considered. The results of the proposed method are in good agreement with reference methods, and in some cases the GSA-FEM is more efficient. Moreover, the simple structure of GSA and its exploration and exploitation features avoids trapping in a local optimum.

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Wan, Youchuan, Mingwei Wang, Zhiwei Ye, and Xudong Lai. "A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm." Computational Intelligence and Neuroscience 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/8179670.

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Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

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Le, Dinh-Viet, Ngoc-Phuong Pham, Quang-Phuc Nguyen, and Cao-Tho Phan. "Proposing Binary Gravitational Search Algorithm Parameters for Back-calculation of Road Pavement Moduli." IOP Conference Series: Materials Science and Engineering 1289, no. 1 (August 1, 2023): 012061. http://dx.doi.org/10.1088/1757-899x/1289/1/012061.

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Abstract A Falling Weight Deflectometer is popular equipment to measure surface deflections under imposed loadings, providing the necessary parameters for back-calculating the elastic moduli of road pavements. There are several back-calculation programs available that accurately back-calculate pavement layer moduli. The Gravitational Search Algorithm (GSA), a metaheuristic optimization algorithm inspired by Newton’s law of universal gravitation, is one such algorithm. The Binary Gravitational Search Algorithm (BGSA) is an enhancement algorithm based on GSA that can be used as an efficient search algorithm for back-calculating pavement moduli based on matching measured and calculated surface deflection of road pavements. Choosing the best BGSA parameters is critical for accurate back-calculation of road pavement moduli. Nevertheless, there has not been much study on selecting the best BGSA parameters for back-calculating road pavement moduli in the literature. Therefore, this study proposes strategies for selecting BGSA parameters based on the least computational effort and the least root mean square error between measured and calculated road pavement surface deflections. In this study, the Burmister theory is discussed and the Richardson extrapolation is adopted to improve the accuracy of the calculated points near the pavement surface; the best parameter of BGSA including the agent size A of 50 and an iteration step T of 300 are suggested to back-calculating the road pavement moduli.

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Cheema, Sikander Singh, Amardeep Singh, and Hassène Gritli. "Optimal Crop Selection Using Gravitational Search Algorithm." Mathematical Problems in Engineering 2021 (April 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/5549992.

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For the economic growth of the crop, the optimal utilization of soil is found to be an open area of research. An efficient utilization includes various advantages such as watershed insurance, expanded biodiversity, and reduction of provincial destitution. Generally, soils present synthetic confinements for crop improvement. Therefore, in this paper, a novel diversified crop model is proposed to predict the suitable soil for good production of the crop. The proposed model utilizes a quantum value-based gravitational search algorithm (GSA) to optimize the best solution. Various features of soil are required to be investigated before crop selection. These features are refined further by applying quantum optimization. The crop selection based upon the soil requirement does not require any additional fertilizers which will reduce the production cost. Thus, the proposed model can select the optimal crop according to the soil components using the gravitational search algorithm. Therefore, the gravitational search algorithm is applied to the quantum values obtained from the crop and soil dataset. Extensive experiments show that the proposed model achieves an optimal selection of crops.

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Muhammad, Badaruddin, Zuwairie Ibrahim, Mohd Falfazli Mat Jusof, Nor Azlina Ab Aziz, Nor Hidayati Abd Aziz, and Norrima Mokhtar. "A Hybrid Simulated Kalman Filter - Gravitational Search Algorithm (SKF-GSA)." Proceedings of International Conference on Artificial Life and Robotics 22 (January 19, 2017): 707–10. http://dx.doi.org/10.5954/icarob.2017.gs11-5.

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41

Durairaj, M., and S. Gowri. "Gravitational Search Algorithm - Based Optimization of Process Parameters in Micro Turning Process." Applied Mechanics and Materials 592-594 (July 2014): 391–94. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.391.

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Micro turning is a scaled down version of conventional turning process, but operating on the micro scale of machining parameters to produce micro components. This paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Two conflicting objectives, surface roughness and tool flank wear, are simultaneously optimized. Full factorial experiments were taken with several combinations of cutting speed, feed and depth of cut. In this report, a new optimization algorithm based on the law of gravitation and mass interactions, namely Gravitational Search Algorithm (GSA) is aimed to predict the optimal parameter conditions for controlling tool flank wear and better surface finish.

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42

Milovanović, Miloš, Jordan Radosavljević, and Bojan Perović. "Optimal Distributed Generation Allocation in Distribution Systems with Non-Linear Loads Using a New Hybrid Meta-Heuristic Algorithm." B&H Electrical Engineering 13, no. 1 (December 1, 2019): 4–13. http://dx.doi.org/10.2478/bhee-2019-0001.

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Abstract This paper presents a new hybrid meta-heuristic algorithm based on the Phasor Particle Swarm Optimization (PPSO) and Gravitational Search Algorithm (GSA) for optimal allocation of distributed generation (DG) in distribution systems with non-linear loads. Performance of the algorithm is evaluated on the IEEE 69-bus system with the aim of reducing power losses, as well as improving voltage profile and power quality. Results, obtained using the proposed algorithm, are compared with those obtained using the original PSO, PPSO, GSA and PSOGSA algorithms. It is found that the proposed algorithm has better performance in terms of convergence speed and finding the best solutions.

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Barzegar, Behnam, Homayun Motameni, and Hossein Bozorgi. "Solving Flexible Job-Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net." Journal of Applied Mathematics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/651310.

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Анотація:

Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN) that is based on gravitational search algorithm (GSA). In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.

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Illias, Hazlee Azil, Ming Ming Lim, Ab Halim Abu Bakar, Hazlie Mokhlis, Sanuri Ishak, and Mohd Dzaki Mohd Amir. "Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence." PLOS ONE 16, no. 7 (July 1, 2021): e0253967. http://dx.doi.org/10.1371/journal.pone.0253967.

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In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.

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Hardiansyah, Hardiansyah. "Hybrid PSOGSA technique for solving dynamic economic emission dispatch problem." Engineering review 40, no. 3 (May 21, 2020): 96–104. http://dx.doi.org/10.30765/er.40.3.10.

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In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

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Et.al, Samuel Jonas Yeboah. "Gravitational Search Algorithm Based Automatic Load Frequency Control for Multi-Area Interconnected Power System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 4548–68. http://dx.doi.org/10.17762/turcomat.v12i3.1845.

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Demand and frequency deviation is gaining more popularity in power system research especially with multiple power systems interconnections and operations as a result of the complexity of power system network, network upgrade and renewable energy sources integration. However, stability of the power system with respect to momentarily fault of Load Frequency Control (LFC) models, in terms of time taken for the fault to settle, magnitude of overshoot and Steady-State Error (SSE) margin, still remain a challenge to the various proposed LFC designs for power system stability. This paper proposes an intelligent demand and frequency variations controller for a four-area interconnected power system using Gravitational Search Algorithm (GSA) optimisation technique. Proportional Integral Derivative (PID) controller and Gravitational Search Algorithm (GSA) were integrated and implemented on the interconnected power system. The optimised GSA-PID controller demonstrated robustness and superiority with time taken for the instability to settle and maximum overshoot in all the four areas as compared to results with Particle Swarm Optimisation (PSO) PID controller and conventional PID controller under 1% and 5% load perturbation. The settling time in all the areas produced tremendous results with GSA-PID controller compared to the results of PSO-PID and conventional PID, the performance of GSA-PID controller shows better dynamic responses with superior damping, less overshoot, minimum oscillations and shorter transient duration.

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Dehghani, Mohammad, Zeinab Montazeri, Gaurav Dhiman, O. P. Malik, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, Ali Dehghani, Josep M. Guerrero, and Lizeth Parra-Arroyo. "A Spring Search Algorithm Applied to Engineering Optimization Problems." Applied Sciences 10, no. 18 (September 4, 2020): 6173. http://dx.doi.org/10.3390/app10186173.

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Анотація:

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.

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Karuppiah, N., S. Muthubalaji, S. Ravivarman, Md Asif, and Abhishek Mandal. "Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 203. http://dx.doi.org/10.14419/ijet.v7i4.6.20463.

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Flexible Alternating Current Transmission System devices have numerous applications in electrical transmission lines like improvement of voltage stability, reactive power compensation, congestion management, Available Transfer Capacity enhancement, real power loss reduction, voltage profile improvement and much more. The effectiveness of these FACTS devices is enhanced by the placement of these devices in the transmission lines. The placement is based on transmission line sensitivity factors such as Bus voltage stability index and line voltage stability index. This research article focuses on optimizing the location, number and ratings of FACTS devices using Evolutionary Algorithms like Bacterial Foraging Algorithm and Gravitational search algorithm. FACTS devices such as Static Var Compensator, Thyristor Controlled Series Capacitor and Unified Power Flow Controller are placed on IEEE 14 bus and IEEE 30 bus systems for reducing the real power loss in the transmission system. The results show that the performance of the transmission lines is enhanced more using Bacterial Foraging Algorithm than Gravitational Search Algorithm.

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Yuan, Xiaohui, Zhihuan Chen, Yanbin Yuan, Yuehua Huang, and Xiaopan Zhang. "A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1559010. http://dx.doi.org/10.1142/s0218001415590107.

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A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

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Arıcı, FerdaNur, and Ersin Kaya. "Comparison of Meta-heuristic Algorithms on Benchmark Functions." Academic Perspective Procedia 2, no. 3 (November 22, 2019): 508–17. http://dx.doi.org/10.33793/acperpro.02.03.41.

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Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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