A hybrid evolutionary-simplex search method to solve nonlinear constrained optimization problems

2019 ◽  
Vol 23 (22) ◽  
pp. 12001-12015 ◽  
Author(s):  
Alyaa Abdelhalim ◽  
Kazuhide Nakata ◽  
Mahmoud El-Alem ◽  
Amr Eltawil
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Search Method ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization ◽  
Simplex Search ◽  
Simplex Search Method

An Efficient Hybrid Evolution Strategy Algorithm with Direct Search Method for Global Optimization

2019 ◽  
Vol 9 (3) ◽  
pp. 63-78
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Global Optimization ◽  
Adaptive Evolution ◽  
Optimization Problems ◽  
Search Method ◽  
Local Solution ◽  
Evolution Strategy ◽  
Direct Search Method ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Restart Strategy

The main purpose of this article is to demonstrate how evolution strategy optimizers can be improved by incorporating an efficient hybridization scheme with restart strategy in order to jump out of local solution regions. The authors propose a hybrid (μ, λ)ES-NM algorithm based on the Nelder-Mead (NM) simplex search method and evolution strategy algorithm (ES) for unconstrained optimization. At first, a modified NM, called Adaptive Nelder-Mead (ANM) is used that exhibits better properties than standard NM and self-adaptive evolution strategy algorithm is applied for better performance, in addition to a new contraction criterion is proposed in this work. (μ, λ)ES-NM is balancing between the global exploration of the evolution strategy algorithm and the deep exploitation of the Nelder-Mead method. The experiment results show the efficiency of the new algorithm and its ability to solve optimization problems in the performance of accuracy, robustness, and adaptability.

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.

Development of seven hybrid methods based on collective intelligence for solving nonlinear constrained optimization problems

2016 ◽  
Vol 49 (2) ◽  
pp. 245-279 ◽  
Author(s):  
Sergio Gerardo de-los-Cobos-Silva ◽  
Roman Anselmo Mora-Gutiérrez ◽  
Miguel Angel Gutiérrez-Andrade ◽  
Eric Alfredo Rincón-García ◽  
Antonin Ponsich ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Hybrid Methods ◽  
Collective Intelligence ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization
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