Steady-state evolutionary algorithm for solving constrained optimization problems
Ziyi Chen, Lishan Kang · 2006
A novel steady-state evolutionary algorithm (MEA) is proposed to solve the constrained global optimization problems. MEA adopts the partial ordering scheme to handle the equality constraints and inequality constraints in a universal way. Meanwhile,a novel multi-parent crossover operator which can instruct its search direction using statistical information is presented to accelerate the convergence. Experiments have been carried on several benchmark functions to test the performance of the presented MEA. Numerical results show that MEA is highly competitive with other algorithms in effectiveness and generality.