Any evolutionary technique for multimodal optimization must answer two crucial 
questions in order to guarantee some success on a given task: How to most 
unboundedly distinguish between the different attraction basins and how to most 
accurately safeguard the consequently discovered solutions. This paper thus 
aims to present a novel technique that integrates the conservation of the best 
successive local individuals (as in the species conserving genetic algorithm) 
with a topological subpopulations separation (as in the multinational genetic 
algorithm) instead of the common but problematic radius-triggered manner. A 
special treatment for offspring integration, a more rigorous control on the 
allowed number and uniqueness of the resulting seeds, and a more efficient 
fitness evaluations budget management further augment a previously suggested 
naive combination of the two algorithms. Experiments have been performed on 
a series of benchmark test functions, including a problem from engineering 
design. Comparison is primarily conducted to show the significant performance 
difference to the naïve combination; also the related radius-dependent 
conserving algorithm is subsequently addressed. Additionally, three more 
multimodal evolutionary methods, being either conceptually close, competitive 
as radius-based strategies, or recent state-of-the-art are also taken into 
account. We detect a clear advantage of three of the six algorithms that, 
in the case of our method, probably comes from the proper topological separation 
into subpopulations according to the existing attraction basins, independent of 
their locations in the function landscape. Additionally, an investigation of the 
parameter independence of the method as compared to the radius-compelled 
algorithms is systematically accomplished.