An improved genetic algorithm for nonlinear programming problems with inequality constraints

2005 ◽  
Author(s):  
Wei-Yi Qian ◽  
Ying Wang ◽  
De-Gang Chen
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Inequality Constraints ◽  
Improved Genetic Algorithm

scholarly journals OPTIMIZATION OF PIECEWISE NON-LINEAR MULTI CONSTRAINED ECONOMIC POWER DISPATCH PROBLEM USING AN IMPROVED GENETIC ALGORITHM

2010 ◽  
pp. 320-326
Author(s):  
B. Padmanabhan ◽  
R. S. SivaKumar ◽  
J. Jasper
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Local Optimum ◽  
Improved Genetic Algorithm ◽  
Non Linear ◽  
Equality And Inequality Constraints ◽  
Loading Effects ◽  
Economic Power Dispatch

In this paper, a more realistic formulation of the Economic Dispatch problem is proposed, which considers practical constraints and non linear characteristics. The proposed ED formulation includes ramp rate limits, valve loading effects, equality and inequality constraints, which usually are found simultaneously in realistic power systems. This paper presents a novel Genetic Algorithm to solve the economic load dispatch (ELD) problem of thermal generators of a power system. This method provides an almost global optimal solution, since they don’t get stuck at local optimum. The proposed method and its variants are validated for the two test systems consisting of 3 and 10 thermal units whose incremental fuel cost functions takes into account the valve-point loading effects.

Improved genetic algorithm for nonlinear programming problems

2011 ◽  
Vol 22 (3) ◽  
pp. 540-546 ◽  
Author(s):  
Kezong Tang ◽  
Jingyu Yang ◽  
Haiyan Chen ◽  
Shang Gao
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Improved Genetic Algorithm

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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