scholarly journals Meta-optimization of the parameters for the gravitational search algorithm in chemical kinetics problems

2021 ◽  
Vol 2131 (2) ◽  
pp. 022005
Author(s):  
L V Enikeeva ◽  
E N Shvareva ◽  
D A Dubovtsev ◽  
I M Gubaydullin
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Chemical Kinetics ◽  
Heuristic Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Parameters ◽  
Gravitational Search ◽  
Meta Optimization ◽  
Selection Of

Abstract The paper considers the selection of the optimal parameters of the heuristic algorithm, which is used for mathematical modeling of the chemical-technological process. This adjustment of the algorithm parameters is called meta-optimization. The gravity search algorithm is used as a heuristic algorithm. Meta-optimization is performed by a genetic algorithm. The meta-optimization is tested on the problem of modeling the process of propane pre-reforming. It is shown that setting even one parameter of a heuristic algorithm is a time-consuming operation. The article presents the results of the numerical solution of the meta-optimization algorithm.

Direct Gravitational Search Algorithm for Global Optimisation Problems

2016 ◽  
Vol 6 (3) ◽  
pp. 290-313 ◽  
Author(s):  
Ahmed F. Ali ◽  
Mohamed A. Tawhid
Keyword(s):  
Heuristic Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Direct Method ◽  
Gravitational Search Algorithm ◽  
Optimal Solution ◽  
Slow Convergence ◽  
Gravitational Search ◽  
Law Of Gravity ◽  
Number Of Iterations

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.

scholarly journals Optimal Crop Selection Using Gravitational Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sikander Singh Cheema ◽  
Amardeep Singh ◽  
Hassène Gritli
Keyword(s):  
Search Algorithm ◽  
Crop Improvement ◽  
Gravitational Search Algorithm ◽  
Crop Selection ◽  
Good Production ◽  
Proposed Model ◽  
Gravitational Search ◽  
Soil Components ◽  
Quantum Optimization ◽  
Selection Of

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.

scholarly journals Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Seyed Reza Nabavi ◽  
Vahid Ostovari Moghadam ◽  
Mohammad Yahyaei Feriz Hendi ◽  
Amirhossein Ghasemi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Multiobjective Genetic Algorithm ◽  
Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

Hybrid Genetic Algorithm-Gravitational Search Algorithm to Optimize Multi-Scale Load Dispatch

2021 ◽  
Vol 12 (3) ◽  
pp. 28-53
Author(s):  
D. Santra ◽  
A. Mukherjee ◽  
K. Sarker ◽  
S. Mondal
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Electrical Power ◽  
Gravitational Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimum Result ◽  
Multi Scale ◽  
Gravitational Search ◽  
Power Generation Systems ◽  
First Time

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.

Public assistance intensification of wind-integrated restructured endowment system using thyristor-controlled series compensator peerless location and gauging using the hybrid Genetic Algorithm-Gravitational Search Algorithm

2019 ◽  
pp. 002072091989420
Author(s):  
J Rajakumar ◽  
Sujatha Balaraman
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Operating Cost ◽  
Hybrid Genetic Algorithm ◽  
Market Model ◽  
Wind Generator ◽  
Gravitational Search ◽  
Thyristor Controlled Series Compensator ◽  
Bus System

In a deregulated electricity market, it may at times become challenging to swift all the essential power which are obligatory to move along the transmission line due to congestion. This paper primly waltz up the finest allotment of thyristor-controlled series compensator in deregulated capacity setup with wind generator by considering the maximization of social welfare cost as objective function. In this work, hybrid market model has been considered and the hybrid algorithm is used as a tool, in which Gravitational Search Algorithm is used for attaining optimal location of thyristor-controlled series compensator as major issue, though Genetic Algorithm-based top-notch outflow of power minimizes operating cost after incorporating thyristor-controlled series compensator and Wind Generator as sub-optimization problem. The coherence of this prospective has been tested and analyzed on modified IEEE 14-bus system and modified IEEE 118-bus system at different loading conditions. The influences on the locational marginal pricing and system voltage have been also investigated in this work and the obtained results are compared with other globally accepted techniques reported in the literary texts.

scholarly journals Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

2017 ◽  
Vol 17 (1) ◽  
pp. 72-86 ◽  
Author(s):  
Hossein Azadi Kherabadi ◽  
Sepehr Ebrahimi Mood ◽  
Mohammad Masoud Javidi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fuzzy Controller ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Gravitational Search ◽  
Optimum Solution

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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