  Plagiarism is one of the leading problems in academic and industrial
environments, which its goal is to find the similar items in a typical document
or source code. This paper proposes an architecture based on a Long Short-Term
Memory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by a
population-based approach for parameter initialization. Gradient-based
optimization algorithms such as back-propagation (BP) are widely used in the
literature for learning process in LSTM, attention mechanism, and feed-forward
neural network, while they suffer from some problems such as getting stuck in
local optima. To tackle this problem, population-based metaheuristic (PBMH)
algorithms can be used. To this end, this paper employs a PBMH algorithm,
artificial bee colony (ABC), to moderate the problem. Our proposed algorithm
can find the initial values for model learning in all LSTM, attention
mechanism, and feed-forward neural network, simultaneously. In other words, ABC
algorithm finds a promising point for starting BP algorithm. For evaluation, we
compare our proposed algorithm with both conventional and population-based
methods. The results clearly show that the proposed method can provide
competitive performance.
