Subdividing Labeling Genetic Algorithm: A new method for solving continuous nonlinear optimization problems

2017 ◽  
Author(s):  
Majid Esmaelian ◽  
Francisco J. Santos-Arteaga ◽  
Madjid Tavana ◽  
Masoumeh Vali
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
New Method

Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems

2010 ◽  
Vol 450 ◽  
pp. 560-563
Author(s):  
Dong Mei Cheng ◽  
Jian Huang ◽  
Hong Jiang Li ◽  
Jing Sun
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Local Convergence ◽  
Population Genetic ◽  
Optimization Problems ◽  
Optimal Solution ◽  
New Method ◽  
Constrained Optimization Problems ◽  
Population Genetic Algorithm

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.

Constrained Nonlinear Optimization in Business

2014 ◽  
pp. 523-535
Author(s):  
William P. Fox
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Nonlinear Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Transportation Networks ◽  
Business Environment ◽  
Traveling Salesman ◽  
Constrained Nonlinear Optimization ◽  
The Traveling Salesman Problem

We present both classical analytical, numerical, and heuristic techniques to solve constrained optimization problems relating to business, industry, and government. We briefly discuss other methods such as genetic algorithm. Today's business environment has many resource challenges to their attempts to maximize profits or minimize costs for which constrained optimization might be used. Facility location and transportation networks techniques are often used as well as the traveling salesman problem.

A New Method to Solve the Kinematic Problems of Parallel Robots Using Generalized Reduced Gradient Algorithm

2016 ◽  
Vol 28 (3) ◽  
pp. 404-417 ◽  
Author(s):  
Thanh Trung Trang ◽  
◽  
Wei Guang Li ◽  
Thanh Long Pham ◽  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Computing Time ◽  
Parallel Manipulators ◽  
Parallel Robots ◽  
New Method ◽  
Gradient Algorithm ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient ◽  
Reduced Gradient Algorithm

[abstFig src='/00280003/17.jpg' width=""300"" text='Stewart Gough robot and the equivalent substitutional configuration' ] This paper proposes a new method of solving the kinematic problems for parallel robots. The paper content aims to solve nonlinear optimization problems with constraints rather than to directly solve high-order nonlinear systems of equations. The nonlinear optimization problems shall be efficiently solved by applying the Generalized Reduced Gradient algorithm and appropriate downgrade techniques. This new method can be able to find exact kinematic solutions by assigning constraints onto the parameters. The procedure can be done without filtering control results from mathematical solution, from which the control time of manipulators can be reduced. The numerical simulation results in this paper shall prove that the method can be applied to solve kinematic problems for a variety of parallel robots regardless of its structures and degree of freedom (DOF). There are several advantages of the proposed method including its simplicity leading to a shorter computing time as well as achieving high accuracy, high reliability, and quick convergence in final results. Hence, the applicability of this method in solving kinematic problems for parallel manipulators is remarkably high.

A novel genetic algorithm based method for solving continuous nonlinear optimization problems through subdividing and labeling

Measurement ◽  
2018 ◽  
Vol 115 ◽  
pp. 27-38 ◽  
Author(s):  
Majid Esmaelian ◽  
Madjid Tavana ◽  
Francisco J. Santos-Arteaga ◽  
Masoumeh Vali
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Optimization ◽  
Optimization Problems
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