  In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed was wFSS, a version of the Fish School Search algorithm
devised specifically to deal with multi-modal search spaces. A base technique
referred as wrFSS was conceived and three variations applying different
constraint handling procedures were also proposed. Tests were performed in
seven problems from CEC 2010 and a comparison with other approaches was carried
out. Results show that the search strategy proposed is able to handle some
heavily constrained problems and achieve results comparable to the
state-of-the-art algorithms. However, we also observed that the local search
operator present in wFSS and inherited by wrFSS makes the fitness convergence
difficult when the feasible region presents some specific geometrical features.
