A Hopfiled Neural Network for Nonlinear Constrained Optimization Problems Based on Penalty Function

2005 ◽  
pp. 712-717 ◽  
Author(s):  
Zhiqing Meng ◽  
Chuangyin Dang
Keyword(s):  
Neural Network ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization

scholarly journals Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Minggang Dong ◽  
Ning Wang ◽  
Xiaohui Cheng ◽  
Chuanxian Jiang
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Real Life ◽  
Optimization Criterion ◽  
Constrained Optimization Problems ◽  
Constraints Handling ◽  
Efficient Alternative ◽  
Handling Methods

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

scholarly journals A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Alireza Rowhanimanesh ◽  
Sohrab Efati
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Global Optimum ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization ◽  
Evolutionary Methods ◽  
Space Reduction ◽  
Reduced Space

Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red_gen and red_factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.

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