چکیده (انگلیسی):

Optimization methods commonly are designed for solving the
optimization problems. Local search algorithms are optimization method,
which are good candidate in exploiting the search space. However, most of
them need parameter tuning and incapable of escaping from local optima.
This work proposes non-parametric Acceptance Criterion (AC) that not
relies on user-defined, which motivate to propose an Adaptive Acceptance
Criterion (AAC). AC accepts a little worse solution based on comparing the
candidate and best solutions found values to a stored value. The value is
stored based on the lowest value of comparing the candidate and best
solution found, when a new best solution found. AAC adaptively escape
from local optima by employing a similar diversification idea of a previous
proposed (ARDA) algorithm. In AAC, an estimated value added to the
threshold (when the search is idle) to increase the search exploration. The
estimated value is generated based on the frequency of the solutions quality
differences, which are stored in an array. The progress of the search
diversity is governed by the stored value. Six medical benchmark datasets
for clustering problem (which are available in UCI Machine Learning
Repository) and eleven benchmark datasets for university course
timetabling problems (Socha benchmark datasets) are used as test domains.
In order to evaluate the effectiveness of the propose AAC, comparison
made between AC, AAC and other approaches drawn from the scientific
literature. Results indicate that, AAC algorithm is able to produce good
quality solutions which are comparable to other approaches in the literature.