ANALYSING THE IMPACT OF PENALTY CONSTANT ON PENALTY FUNCTION THROUGH PARTICE SWARM OPTIMIZATION

Constrained Evolutionary Optimization - the penalty function approach

Combining Penalty Function with Modified Chicken Swarm Optimization for Constrained Optimization

In many mechanical designs, such as airborne electro-optical platform, optical lenses, mechanical containers, speed reducer, and so on, lightweight design has always been our goal. Under various constraints, obtaining the minimum of some parameter is the optimization problem we often encounter in the engineering works. Chicken Swarm Optimization (CSO), a new bioinspired algorithm, is namely app...

Decrease of the Penalty Parameter in Differentiable Penalty Function Methods

We propose a simple modification to the differentiable penalty methods for solving nonlinear programming problems. This modification decreases the penalty parameter and the ill-conditioning of the penalty method and leads to a faster convergence to the optimal solution. We extend the modification to the augmented Lagrangian method and report some numerical results on several nonlinear programmi...

Smoothed Lower Order Penalty Function for Constrained Optimization Problems

The paper introduces a smoothing method to the lower order penalty function for constrained optimization problems. It is shown that, under some mild conditions, an optimal solution of the smoothed penalty problem is an approximate optimal solution of the original problem. Based on the smoothed penalty function, an algorithm is presented and its convergence is proved under some mild assumptions....

Adaptive Penalty Function for Solving Constrained Evolutionary Optimization

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. The penalty function approach is generic and applicable to any type of constraint (linear or nonlinear). Nonetheless, the most diffic...