Abstract:
The paper compares two well-known multiobjective memetic algorithms through
computational experiments on 0/1 knapsack problems. The two algorithms are
MOGLS (multiple objective genetic local search) of Jaszkiewicz and M-PAES
(memetic Pareto archived evolution strategy) of Knowles & Corne. It is shown
that the MOGLS with a sophisticated repair algorithm based on the current
weight vector in the scalar fitness function has much higher search ability
than the M-PAES with a simple repair algorithm. When they use the same
simple repair algorithm, the M-PAES performs better overall. It is also shown
that the diversity of non-dominated solutions obtained by the MPAES is small in
comparison with the MOGLS. For improving the performance of the M-PAES, we
examine the use of the scalar fitness function with a random weight
vector in the selection procedure of parent solutions.