scholarly journals An Adaptive Penalty Method for Genetic Algorithms in Constrained Optimization Problems

10.5772/5446 ◽  
2008 ◽  
Author(s):  
Helio J. C. Barbosa ◽  
Afonso C. C. Lemonge
Keyword(s):  
Genetic Algorithms ◽  
Constrained Optimization ◽  
Penalty Method ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

scholarly journals AN ADAPTIVE CONSTRAINT HANDLING TECHNIQUE FOR PARTICLE SWARM IN CONSTRAINED OPTIMIZATION PROBLEMS

2016 ◽  
Vol 8 (1) ◽  
pp. 39 ◽  
Author(s):  
Érica Da Costa Reis Carvalho ◽  
José Pedro Gonçalves Carvalho ◽  
Heder Soares Bernardino ◽  
Patrícia Habib Hallak ◽  
Afonso Celso de Castro Lemonge
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Test Problems ◽  
Constrained Optimization Problems ◽  
Comparison Results ◽  
Engineering Problems ◽  
Adaptive Penalty ◽  
Handling Technique ◽  
Adaptive Penalty Method

Nature inspired meta-heuristics are largely used to solve optimization problems. However, these techniques should be adapted when solving constrained optimization problems, which are common in real world situations. Here an adaptive penalty approach (called Adaptive Penalty Method, APM) is combined with a particle swarm optimization (PSO) technique to solve constrained optimization problems. This approach is analyzed using a benchmark of test-problems and 5 mechanical engineering problems. Moreover, three variants of APM are considered in the computational experiments. Comparison results show that the proposed algorithm obtains a promising performance on the majority of the test problems

scholarly journals An Augmented Lagrangian Method for Cardinality-Constrained Optimization Problems

2021 ◽  
Author(s):  
Christian Kanzow ◽  
Andreas B. Raharja ◽  
Alexandra Schwartz
Keyword(s):  
Constrained Optimization ◽  
Numerical Experiments ◽  
Penalty Method ◽  
Augmented Lagrangian ◽  
Constraint Qualification ◽  
Optimization Problems ◽  
Augmented Lagrangian Method ◽  
Constrained Optimization Problems ◽  
Strongly Stationary ◽  
Type Constraint

AbstractA reformulation of cardinality-constrained optimization problems into continuous nonlinear optimization problems with an orthogonality-type constraint has gained some popularity during the last few years. Due to the special structure of the constraints, the reformulation violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast to this, we investigate the viability of using a standard safeguarded multiplier penalty method without any problem-tailored modifications to solve the reformulated problem. We prove global convergence towards an (essentially strongly) stationary point under a suitable problem-tailored quasinormality constraint qualification. Numerical experiments illustrating the performance of the method in comparison to regularization-based approaches are provided.

scholarly journals On Heaviside Binary Logic: a Mathematical Characterization and its Applications

2020 ◽  
Author(s):  
Giovanni Iacovelli ◽  
Claudio Iacovelli
Keyword(s):  
Constrained Optimization ◽  
Programming Language ◽  
Penalty Method ◽  
Optimization Problems ◽  
Mathematical Formulation ◽  
Logical Circuit ◽  
Constrained Optimization Problems ◽  
Control Structures ◽  
Binary Logic ◽  
Language Control

In this work, the existence of a correspondence between fundamental programming language control structures and mathematical formulation is proven. The proposed interpretation is given through a well-defined logical circuit analytical expression. Relevant geometrical applications having wide implications in engineering branches are presented together with a new Penalty Method for constrained optimization problems handling.

A Constrained Particle Swarm Optimization Algorithm with Oracle Penalty Method

2013 ◽  
Vol 303-306 ◽  
pp. 1519-1523 ◽  
Author(s):  
Ming Gang Dong ◽  
Xiao Hui Cheng ◽  
Qin Zhou Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Penalty Method ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Swarm Optimization

To solve constrained optimization problems, an Oracle penalty method-based comprehensive learning particle swarm optimization (OBCLPSO) algorithm was proposed. First, original Oracle penalty was modified. Secondly, the modified Oracle penalty method was combine with comprehensive learning particle swarm optimization algorithm. Finally, experimental results and comparisons were given to demonstrate the optimization performances of OBCLPSO. The results show that the proposed algorithm is a very competitive approach for constrained optimization problems.

scholarly journals A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

2018 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
D B Fatemeh ◽  
C K Loo ◽  
G Kanagaraj ◽  
S G Ponnambalam
Keyword(s):  
Penalty Method ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Computational Time ◽  
Shuffled Complex Evolution ◽  
Centroidal Voronoi Tessellations ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time.

scholarly journals On Heaviside Binary Logic: a Mathematical Characterization and its Applications

2020 ◽  
Author(s):  
Giovanni Iacovelli ◽  
Claudio Iacovelli
Keyword(s):  
Constrained Optimization ◽  
Programming Language ◽  
Penalty Method ◽  
Optimization Problems ◽  
Mathematical Formulation ◽  
Logical Circuit ◽  
Constrained Optimization Problems ◽  
Control Structures ◽  
Binary Logic ◽  
Language Control

In this work, the existence of a correspondence between fundamental programming language control structures and mathematical formulation is proven. The proposed interpretation is given through a well-defined logical circuit analytical expression. Relevant geometrical applications having wide implications in engineering branches are presented together with a new Penalty Method for constrained optimization problems handling.

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