An adaptive penalty method for DIRECT algorithm in engineering optimization

2012 ◽  
Author(s):  
Rita Vilaça ◽  
Ana Maria A. C. Rocha
Keyword(s):  
Penalty Method ◽  
Direct Algorithm ◽  
Engineering Optimization ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Differential evolution with the adaptive penalty method for structural multi-objective optimization

2018 ◽  
Vol 20 (1) ◽  
pp. 65-88 ◽  
Author(s):  
Dênis E. C. Vargas ◽  
Afonso C. C. Lemonge ◽  
Helio J. C. Barbosa ◽  
Heder S. Bernardino
Keyword(s):  
Differential Evolution ◽  
Penalty Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems

2006 ◽  
Author(s):  
Berge Djebedjian ◽  
Ashraf Yaseen ◽  
Magdy Abou Rayan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Penalty Method ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimization Problem ◽  
Water Distribution Systems ◽  
Building Blocks ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty function. The optimal design of water distribution systems is a constrained non-linear optimization problem. Constraints (for example, the minimum pressure requirements at the nodes) are generally handled within genetic algorithm optimization by introducing a penalty cost function. The optimal solution is found when the pressures at some nodes are close to the minimum required pressure. The goal of an adaptive penalty function is to change the value of the penalty draw-down coefficient during the search allowing exploration of infeasible regions to find optimal building blocks, while preserving the feasibility of the final solution. In this study, a new penalty coefficient strategy is assumed to increase with the total cost at each generation and inversely with the total number of nodes. The application of the computer program to case studies shows that it finds the least cost in a favorable number of function evaluations if not less than that in previous studies and it is computationally much faster when compared with other studies.

scholarly journals Otimização estrutural de treliças considerando restrições de frequências naturais de vibração

2020 ◽  
Vol 18 (2) ◽  
pp. 14
Author(s):  
Afonso Celso de Castro Lemonge ◽  
Patrícia Habib Hallak ◽  
José Pedro Gonçalves Carvalho
Keyword(s):  
Particle Swarm Optimization ◽  
Penalty Method ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Este artigo apresenta um Algoritmo Evolucionário (AE) baseado no comportamento de enxame de partículas (Particle Swarm Optimization - PSO) adaptado para a obtenção de soluções de problemas de otimização estrutural com restrições. O PSO é um algoritmo de fácil implementação e competitivo perante os demais algoritmos populacionais inspirados na natureza. Neste artigo, são analisados problemas de otimização estrutural de treliças submetidas a restrições de frequências naturais de vibração. Para o tratamento destas restrições, incorpora-se ao PSO uma técnica de penalização adaptativa (Adaptive Penalty Method - APM), que tem demonstrado robustez e eficiência quando aplicada no tratamento de problemas de otimização com restrições. O algoritmo proposto é validado através de experimentos computacionais em problemas de otimização estrutural amplamente discutidos na literatura.

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.

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