Design of a simple evolutionary multiobjective optimization method based on the combined use of scalarization and particle swarm optimization

This paper proposes a general methodology of multiobjective optimization based on the combined use of scalarization and evolutionary computation approaches. Mathematically, it is guaranteed that a Pareto optimal solution of a multiobjective optimization problem (MOP) can be found by minimizing the corresponding augmented Tchebysheff scalarized function. In this way, different Pareto optimal solutions can be obtained by solving different single‐objective optimization problems (SOPs) with different weights for scalarization. In order to ascertain the global structure of the Pareto optimal set and/or the Pareto frontier of an MOP with simple computation, we propose the basic concept that (a) the SOPs with different weight should be solved independently by some evolutionary computation algorithms; and (b) previously obtained useful solutions should be partly reused for the optimization of another SOP. In particular, for continuous‐variable MOPs, we also propose a practical computation method based on the concept, which uses particle swarm optimization (PSO) with an effective reinitialization mechanism. The usefulness of the proposed methodology is demonstrated through numerical experiments with the proposed computation method. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(11): 1–13, 2012; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/ecj.11437