Comparing Two Approaches for Solving Constrained Global Optimization Problems

2017 ◽  
pp. 301-306
Author(s):  
Konstantin Barkalov ◽  
Ilya Lebedev
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Constrained Global Optimization

scholarly journals A new auxiliary function approach for inequality constrained global optimization problems

2019 ◽  
Vol 9 (3) ◽  
pp. 31-38
Author(s):  
Nurullah Yilmaz ◽  
Ahmet Sahiner
Keyword(s):  
Global Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Auxiliary Function ◽  
Exact Penalty Function ◽  
Function Approach ◽  
Exact Penalty ◽  
Constrained Global Optimization ◽  
Constrained Optimization Problems ◽  
Smooth Exact Penalty Function

In this study, we deal with the nonlinear constrained global optimization problems. First, we introduce a new smooth exact penalty function for constrained optimization problems. We combine the exact penalty function with the auxiliary function in regard to constrained global optimization. We present a new auxiliary function approach and the adapted algorithm for solving  non-linear inequality constrained global optimization problems. Finally, we illustrate the efficiency of the algorithm on some numerical examples.

The spherical search algorithm for bound-constrained global optimization problems

2019 ◽  
Vol 85 ◽  
pp. 105734 ◽  
Author(s):  
Abhishek Kumar ◽  
Rakesh Kumar Misra ◽  
Devender Singh ◽  
Sujeet Mishra ◽  
Swagatam Das
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Constrained Global Optimization

scholarly journals Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches

2012 ◽  
Vol 2012 ◽  
pp. 1-36 ◽  
Author(s):  
Jui-Yu Wu
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Constrained Global Optimization ◽  
Generalized Polynomial ◽  
Real Coded Genetic Algorithm ◽  
Constriction Coefficient ◽  
Optimum Solution

This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.

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