Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation

2014 ◽  
Vol 81 ◽  
pp. 504-519 ◽  
Author(s):  
Hao Tian ◽  
Xiaohui Yuan ◽  
Bin Ji ◽  
Zhihuan Chen
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multi Objective Optimization ◽  
Short Term ◽  
Hydrothermal Scheduling ◽  
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.

Nondominated sorting-based disruption in oppositional gravitational search algorithm for stochastic multiobjective short-term hydrothermal scheduling

2018 ◽  
Vol 23 (16) ◽  
pp. 7229-7248 ◽  
Author(s):  
Gouthamkumar Nadakuditi ◽  
Srinivasarao Balusu ◽  
Venkateswararao Bathina ◽  
P. V. R. L. Narasimham
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Short Term ◽  
Hydrothermal Scheduling ◽  
Gravitational Search ◽  
Nondominated Sorting

A Multi-Objective Gravitational Search Algorithm Based on Non-Dominated Sorting

2012 ◽  
Vol 3 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Hadi Nobahari ◽  
Mahdi Nikusokhan ◽  
Patrick Siarry
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Acceleration ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mutation Operators ◽  
External Archive ◽  
Gravitational Search ◽  
New Criterion

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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