Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation

Abstract

In the last two decades, multiobjective optimization has become main stream and various multiobjective 
evolutionary algorithms (MOEAs) have been suggested in the field of evolutionary computing (EC) for 
solving hard combinatorial and continuous multiobjective optimization problems. Most MOEAs employ single 
evolutionary operators such as crossover, mutation and selection for population evolution. In this 
paper, we suggest a multiobjective evolutionary algorithm based on multimethods (MMTD) with dynamic resource 
allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm 
employs two well known population based stochastic algorithms namely MOEA/D and NSGA-II as constituent 
algorithms for population evolution with a dynamic resource allocation scheme. We have examined the performance 
of the proposed MMTD on two different MOPs test suites: the widely used ZDT problems and the recently formulated 
test instances for the special session on MOEAs competition of the 2009 IEEE congress on evolutionary 
computation (CEC'09). Experimental results obtained by the suggested MMTD are more promising than those of some 
state-of-the-art MOEAs in terms of the inverted generational distance (IGD)-metric on most test problems.