The present paper investigates the hybridization of two well-known
multimodal optimization methods, i.e. species conservation and
multinational algorithms. The topological species conservation
algorithm embraces the vision of the existence of subpopulations
around seeds (the best local individuals) and the preservation of
these dominating individuals from one generation to another, but
detects multimodality by means of the hill-valley mechanism
employed by multinational algorithms. The aim is to inherit the
strengths of both parent techniques and simultaneously to overcome
the flaws of both of them. The species conservation algorithm
efficiently keeps track of several good search space regions at
once, but is difficult to parametrize without prior problem knowledge.
Conversely, the
multinational algorithms use many function evaluations to
establish subpopulations, but do not depend on provided radius
parameter values. Experiments with all three algorithms are
conducted on a wide range of test problems in order to
investigate their advantages and shortcomings.