A logarithmic mutation operator to solve constrained optimization problems

We propose a method based on improving the exploration of the search space to solve constrained optimization problems. This exploration is performed with a mutation operator, whose distribution function is logarithmic. A BLX-0.5 crossover is also used. These operators are associated to a rudimentary constraint handling method implemented by a dedicated ranking selection such that feasible individuals are more likely selected than unfeasible ones.