Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT

2017 ◽  
Vol 25 (1) ◽  
pp. 399-413 ◽  
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad
Keyword(s):  
Energy Efficient ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Energy Efficient Routing ◽  
Multi Objective ◽  
Gravitational Search

scholarly journals An improved multi-objective gravitational search algorithm

2021 ◽  
Vol 1978 (1) ◽  
pp. 012029
Author(s):  
Yuhong Guo ◽  
Teng Luo ◽  
Hongqian Cao
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multi Objective ◽  
Gravitational Search

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

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.

scholarly journals Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2040 ◽  
Author(s):  
Feng ◽  
Liu ◽  
Jiang ◽  
Luo ◽  
Miao
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Multi Objective ◽  
Practical Constraint ◽  
Hydropower System ◽  
Order Preference ◽  
Gravitational Search ◽  
The Individual

In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

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