چکیده (انگلیسی):

Error correcting codes, also known as error controlling codes,
are set of codes with redundancy that allows detecting errors. This is quite
useful in transmitting data over a noisy channel or when retrieving data
from a storage with possible physical defects. The idea is to use a set of
code words that are maximally distant from each other, hence reducing the
chance of changing a valid codeword to another valid codeword by flipping
bits. The problem can be viewed as picking m codes out of 2n available nbit
combinations, such that the aggregate hamming distance among those
codewords is maximized. Due to the large solution spaces of such
problems, greedy algorithms are sometimes used to generate quick and
dirty solutions. However, modern evolutionary search algorithms like
genetic algorithms, swarm particles, gravitational search and others, offer
good alternatives, yielding near optimal solutions in exchange for some
time. Chemical Reaction Optimization (CRO) has emerged as a new
evolutionary algorithm to solve complex optimization problems. This
algorithm mimics the molecular interactions towards finding a minimal
energy state, which corresponds to an optimal solution for the problem in
hand. In this research, we proposed a solution for the maximally distant
codes allocation problem, through a binary knapsack mapping and
compared the performance with the well established Ant Colony
Optimization (ACO) algorithm, which is inspired by the ant’s capability to
find the shortest path between the nest and source of food. The binary
knapsack mapping was used in the two algorithms. Test results showed that
the CRO outperformed the ACO in every metric given any time budget.