This paper presents an augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based m...

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimisation. That implies the evolutionary algorithm should be focused in exploring the search space while the local search method exploits the achieved solutions. To tackle this issue, we propose to maintain a higher diversity in the evolutionary algorithm's pop...

Due to the complex nature of training neural network (NN), this problem has gained popularity in the nonlinear optimization field. In order to avoid falling into local minimum because of inappropriate initial weights, a number of global search techniques are developed. This paper applies a novel global algorithm, which is electromagnetism-like mechanism (EM) algorithm, to train NN and the EM ba...

We present a dissipative algorithm for solving nonlinear wave-like equations when the initial data is specified on characteristic surfaces. The dissipative properties built in this algorithm make it particularly useful when studying the highly nonlinear regime where previous methods have failed to give a stable evolution in three dimensions. The algorithm presented in this work is directly appl...

The most simple evolutionary algorithm, the so-called (1+1)EA accepts a child if its fitness is at least as large (in the case of maximization) as the fitness of its parent. The variant (1 + 1)∗EA only accepts a child if its fitness is strictly larger than the fitness of its parent. Here two functions related to the class of long path functions are presented such that the (1 + 1)EA maximizes on...