Modification of the SCE-UA to Include Constraints by Embedding an Adaptive Penalty Function and Application: Application Approach

2014 ◽  
Vol 28 (8) ◽  
pp. 2145-2159 ◽  
Author(s):  
Taeuk Kang ◽  
Sangho Lee
Keyword(s):  
Penalty Function ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function

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.

scholarly journals Fitness Estimation Based Particle Swarm Optimization Algorithm for Layout Design of Truss Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ayang Xiao ◽  
Benli Wang ◽  
Chaoli Sun ◽  
Shijie Zhang ◽  
Zhenguo Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Penalty Function ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Computational Cost ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function

Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational cost of truss analysis is often quite expensive. In this paper, a novel fitness estimation based particle swarm optimization algorithm with an adaptive penalty function approach (FEPSO-AP) is proposed to handle this problem. FEPSO-AP adopts a special fitness estimate strategy to evaluate the similar particles in the current population, with the purpose to reduce the computational cost. Further more, a laconic adaptive penalty function is employed by FEPSO-AP, which can handle multiple constraints effectively by making good use of historical iteration information. Four benchmark examples with fixed topologies and up to 44 design dimensions were studied to verify the generality and efficiency of the proposed algorithm. Numerical results of the present work compared with results of other state-of-the-art hybrid algorithms shown in the literature demonstrate that the convergence rate and the solution quality of FEPSO-AP are essentially competitive.

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