IEEE Transactions on Evolutionary Computation Outstanding 2010 Paper Award (bestowed in 2013)

In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we sug- gest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective op- timization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to new memetic (or hybrid) algorithms. The pecularity of the HCS is that it is intended to be capable both moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. The local search procedure utilizes the geometry of the directional cones of such optimization prob- lems and works with or without gradient information. Finally, we present some numerical results on some well-known benchmark problems, indi- cating the strength of the local search strategy as a standalone algorithm as well as its benefit when used within a MOEA. For the latter we use the state of the art algorithms Nondominated Sorting Genetic Algorithm-II and Strength Pareto Evolutionary Algorithm 2 as base MOEAs.