  A lot of real-world engineering problems represent dynamicity with nests of
nonlinearities due to highly complex network of exponential functions or large
number of differential equations interacting together. Such search spaces are
provided with multiple convex regions peaked with diverse nonlinear slopes and
in non-homogenous ways. To find global optima, a new meta-heuristic algorithm
is proposed based on Observer Effect concepts for controlling memory usage per
localities without pursuing Tabu-like cut-off approaches. Observer effect in
physics (or psychology) regards bias in measurement (or perception) due to the
interference of instrument (or knowledge). Performance analysis of the proposed
algorithms is sought in two real-world engineering applications, i.e.,
Electroencephalogram feature learning and Distributed Generator parameter
tuning, each of which having nonlinearity and complex multi-modal peaks
distributions as their characteristics. In addition, the effect of version
improvement has been assessed. The performance comparison with other optimizers
in the same context suggests that proposed algorithm is useful both solely and
in hybrid Gradient Descent settings where problem's search space is
nonhomogeneous in terms of local peaks density.
