Hybrid Genetic Algorithm with an Adaptive Penalty Function for Fitting Multimodal Experimental Data: Application to Exchange-Coupled Non-Kramers Binuclear Iron Active Sites

2011 ◽  
Vol 51 (9) ◽  
pp. 2164-2173 ◽  
Author(s):  
Eric Beaser ◽  
Jennifer K. Schwartz ◽  
Caleb B. Bell ◽  
Edward I. Solomon
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Penalty Function ◽  
Active Sites ◽  
Hybrid Genetic Algorithm ◽  
Data Application ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function ◽  
Iron Active Sites ◽  
Binuclear Iron

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 A Tabu Search-Based Memetic Algorithm for Hardware/Software Partitioning

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Geng Lin ◽  
Wenxing Zhu ◽  
M. Montaz Ali
Keyword(s):  
Tabu Search ◽  
Penalty Function ◽  
Memetic Algorithm ◽  
Feedback Mechanism ◽  
Solution Generation ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function ◽  
Local Search Procedure ◽  
Partitioning Problem ◽  
Unconstrained Problem

Hardware/software (HW/SW) partitioning is to determine which components of a system are implemented on hardware and which ones on software. It is one of the most important steps in the design of embedded systems. The HW/SW partitioning problem is an NP-hard constrained binary optimization problem. In this paper, we propose a tabu search-based memetic algorithm to solve the HW/SW partitioning problem. First, we convert the constrained binary HW/SW problem into an unconstrained binary problem using an adaptive penalty function that has no parameters in it. A memetic algorithm is then suggested for solving this unconstrained problem. The algorithm uses a tabu search as its local search procedure. This tabu search has a special feature with respect to solution generation, and it uses a feedback mechanism for updating the tabu tenure. In addition, the algorithm integrates a path relinking procedure for exploitation of newly found solutions. Computational results are presented using a number of test instances from the literature. The algorithm proves its robustness when its results are compared with those of two other algorithms. The effectiveness of the proposed parameter-free adaptive penalty function is also shown.

An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming

2000 ◽  
Vol 4 (2) ◽  
pp. 164-170 ◽  
Author(s):  
Xinghuo Yu ◽  
◽  
Baolin Wu
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Function Method ◽  
Optimization Problems ◽  
Evolutionary Programming ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function

In this paper, we propose a novel adaptive penalty function method for constrained optimization problems using the evolutionary programming technique. This method incorporates an adaptive tuning algorithm that adjusts the penalty parameters according to the population landscape so that it allows fast escape from a local optimum and quick convergence toward a global optimum. The method is simple and computationally effective in the sense that only very few penalty parameters are needed for tuning. Simulation results of five well-known benchmark problems are presented to show the performance of the proposed method.

Theoretical Study on Methane Oxidation Catalyzed by Fe/ZSM-5: The Significant Role of Water on Binuclear Iron Active Sites

2016 ◽  
Vol 120 (48) ◽  
pp. 27422-27429 ◽  
Author(s):  
Miao He ◽  
Jie Zhang ◽  
Xiu-Liang Sun ◽  
Biao-Hua Chen ◽  
Yang-Gang Wang
Keyword(s):  
Methane Oxidation ◽  
Significant Role ◽  
Active Sites ◽  
Theoretical Study ◽  
Iron Active Sites ◽  
Binuclear Iron

Adaptive Collaborative Optimization Strategy Based on Genetic Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 32-36
Author(s):  
Ji Hong Liu ◽  
Hao Jiang ◽  
Qi Xie
Keyword(s):  
Genetic Algorithm ◽  
System Optimization ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Speed Reducer ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function ◽  
Optimization Function ◽  
Definition Of

The genetic algorithm and the adaptive mechanism are adopted to tackle the inefficiency of optimization and the convergence difficulty of collaborative optimization (CO). Based on the further analysis of collaborative optimization process, the constraint conditions are converged into part of the optimization function. The system optimization model of CO has been reconstructed according to the adaptive penalty function which is based on the information of different disciplines and the transformation of system-level constraints. Therefore, the global and local search capabilities of optimization algorithm and searching efficiency of CO have been improved. Meanwhile, the difficulty of convergence caused by the internal definition of CO has been resolved. Finally, an example of speed reducer is demonstrated to verify the proposed method, showing that the convergence rate and search efficiency have been improved.

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