This article presents a hybrid optimization technique particle swarm optimization-gravitational search algorithm for optimal over-current relay coordination. The main contribution of this article is ...to find the optimized relay settings during variation in environmental factors that affect distributed generation performance. The detailed model of wind and photovoltaic source, modeled in a PSCAD/EMTDC platform is penetrated in a 13-bus distribution system. The optimal relay settings are found for different cases, including change in wind speed, change in penetration level, change in cell temperature, and insolation level of photovoltaic. A comparison of particle swarm optimization-gravitational search algorithm with particle swarm optimization and gravitational search algorithm technique is also done, and it is shown that the particle swarm optimization-gravitational search algorithm is a superior method that can be applied in relay coordination tasks.
•Modeling the economic dispatch of energy hubs.•Proposing a new optimization algorithm namely SAL-TVAC-GSA.•Considering electricity, gas, heat, cool, and compressed air as energy carriers.•Including ...the valve-point loading effect and prohibited zones of electrical power-only units.
This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function.
•Multi-objective, multi-constrain optimization model of load dispatch for microgrid.•Modified gravitational search algorithm and particle swarm optimization algorithm to solve load dispatch.•Ordered ...charging-discharging strategy reducing cost by 13.4%, load variance by 78.8%
With the increasing proportion of electric vehicles in the automobile market, the negative impact of vehicle’s charging on the power system is gradually increasing. The charging-discharging model of vehicles and the multi-objective optimization model of the load dispatch for the microgrid are established. By combining gravitational search algorithm (GSA) and particle swarm optimization (PSO) algorithm, a hybrid modified GSA-PSO (MGSA-PSO) scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles. To improve the global search performance of the GSA algorithm, the proposed scheme introduces the global memory capacity of the PSO into the GSA. At the same time, the hybrid algorithm is improved by designing adaptive inertia vector, learning factor and chaotic initialization population. The load dispatch optimization are implemented and analyzed, including the unordered charging strategy, the ordered charging-discharging strategy, and the ordered charging-discharging strategy with distributed generations. The optimization results show that, under the same weight factor, the ordered charging-discharging strategy can reduce 13.38% of the total cost, 78.77% of the microgrid load variance and improve the safety and economy of the grid. In addition, reasonable scheduling of distributed power output power can further reduce the total cost by 14.06% and the load variance by 22.36%. Further, the effectiveness of the proposed scheme is proved by analyzing the influences of different numbers of electric vehicles and different charging models.
•A combination of Deep Q-Learning algorithm and metaheuristic GSA is offered.•GSA initializes the weights and the biases of the neural networks.•A comparison with classical random, metaheuristic PSO ...and GWO is carried out.•The validation is done on real-time nonlinear servo system position control.•The drawbacks of randomly initialized neural networks are mitigated.
This paper presents a novel Reinforcement Learning (RL)-based control approach that uses a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational Search Algorithm (GSA). The GSA is employed to initialize the weights and the biases of the Neural Network (NN) involved in DQL in order to avoid the instability, which is the main drawback of the traditional randomly initialized NNs. The quality of a particular set of weights and biases is measured at each iteration of the GSA-based initialization using a fitness function aiming to achieve the predefined optimal control or learning objective. The data generated during the RL process is used in training a NN-based controller that will be able to autonomously achieve the optimal reference tracking control objective. The proposed approach is compared with other similar techniques which use different algorithms in the initialization step, namely the traditional random algorithm, the Grey Wolf Optimizer algorithm, and the Particle Swarm Optimization algorithm. The NN-based controllers based on each of these techniques are compared using performance indices specific to optimal control as settling time, rise time, peak time, overshoot, and minimum cost function value. Real-time experiments are conducted in order to validate and test the proposed new approach in the framework of the optimal reference tracking control of a nonlinear position servo system. The experimental results show the superiority of this approach versus the other three competing approaches.
GSA: A Gravitational Search Algorithm Rashedi, Esmat; Nezamabadi-pour, Hossein; Saryazdi, Saeid
Information sciences,
06/2009, Volume:
179, Issue:
13
Journal Article
Peer reviewed
In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the ...law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture ...that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.
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•An optimized deep learning DenseNet121 architecture for the diagnosis of COVID-19 disease is proposed.•The gravitational search optimization (GSA) algorithm was used to select the optimal values for the hyperparameters of the DenseNet121 architecture.•The GSA-DenseNet121-COVID-19 performance was compared to the performance of a CNN architecture called Inception-v3.•The proposed approach achieves a 98% accuracy on the test set.
In this paper, a new hybrid GSA-GA algorithm is presented for the constraint nonlinear optimization problems with mixed variables. In it, firstly the solution of the algorithm is tuned up with the ...gravitational search algorithm and then each solution is upgraded with the genetic operators such as selection, crossover, mutation. The performance of the algorithm is tested on the several benchmark design problems with different nature of the objectives, constraints and the decision variables. The obtained results from the proposed approach are compared with the several existing approaches result and found to be very profitable. Finally, obtained results are verified with some statistical testing.
A pumped storage unit (PSU) is more difficult to control compared to a conventional hydropower generation unit due to the frequent switching of working conditions and the S-shaped characteristics of ...pump turbine. The traditional proportional–integral–derivative (PID) controller typically cannot easily provide high quality control. To overcome these difficulties, a fractional-order PID (FOPID) controller is designed for a PSU in this study. Although the FOPID controller is more effective compared to the traditional PID controller, it is more complex to optimize the parameters of this controller for a pump turbine governing system (PTGS). Thus, a gravitational search algorithm combined with the Cauchy and Gaussian mutation, named as CGGSA, is proposed and used to optimize the FOPID controller parameters. The experimental results indicate that the CGGSA has shown excellent optimization ability compared with some popular meta-heuristics on benchmark functions. Results have also proved that the FOPID-CGGSA controller shows significant advantages over other PID-type controllers with different optimization strategies. Meanwhile the optimally designed controller has shown great potential to improve the control quality of PTGS under multiple water heads.
•Gravitational Search Algorithm (GSA) was used as a search strategy in a Feature Selection approach.•Crossover and Mutation evolutionary operators were used to improve the quality of GSA.•KNN and ...Decision Tree classifiers were used as evaluators.•The results demonstrate the superiority of the proposed algorithm in solving FS problems.
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With recent advancements in data collection tools and the widespread use of intelligent information systems, a huge amount of data streams with lots of redundant, irrelevant, and noisy features are collected and a large number of features (attributes) should be processed. Therefore, there is a growing demand for developing efficient Feature Selection (FS) techniques. Gravitational Search algorithm (GSA) is a successful population-based metaheuristic inspired by Newton’s law of gravity. In this research, a novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks. As an NP-hard problem, FS finds an optimal subset of features from a given set. For the proposed wrapper FS method, both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers are used as evaluators. Eighteen well-known UCI datasets are utilized to assess the performance of the proposed approaches. In order to verify the efficiency of proposed algorithms, the results are compared with some popular nature-inspired algorithms (i.e. Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)). The extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.
•A novel multi-objective scheduling model is developed for the emergency rescue planning problem.•The proposed model minimizes the fire extinguishing time, delay time, and number of fire rescue ...vehicles.•The factor of fire spreading speed is firstly considered in our optimization model.•A revised multi-objective discrete gravitational search slgorithm is designed to produce Pareto solutions.•An empirical case study in Heilongjiang Province, China, is applied.
The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire.
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