Solving an MOO problem may use two different ways. One way consists of
decomposing the initial problem into a sequence of subproblems. Each subproblem is
solved using information from its neighboring subproblems. We solve all the
subproblems simultaneously. A multi-objective evolutionary algorithm based on
decomposition (MOEA/D) applies this approach to the Kursawe’s test function. There
-lattice are used to approximate convex and nonconvex Pareto-optimal fronts. The
metaheuristics or heuristics combine. The incorporation of local search heuristics into
an MOEA illustrates this aspect. In the literature, we find how hybridization can be
designed. There are three ways to hybridize metaheuristics and heuristics. A first
method is to use one algorithm and improve it with other techniques. A second method
is to use multiple operators in an EA, and a third method is to develop MOGA
solutions by implementing efficient local search. Numerous examples of hybridization
exist in the literature. The algorithm M-PAES combines the local search strategy in the
Pareto archived evolution strategy (PAES) with the use of GA. The main algorithm
PSO can be combined with a local and a global search algorithm.
