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Expert Systems with applications
Elseveir
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Abstract: |
This paper presents an alternative global optimization meta-heuristics (MHs) approach, in-
spired by the natural selection theory. The proposed approach depends on the competition
among six MHs that allows generating an offspring, which can breed the high characteristics
of parents since they are unique and competitive. Therefore, this leads to improve the con-
vergence of the solutions towards an optimal solution and also, to avoid the limitations of
other methods that aim to balance between exploitation and exploration. The six algorithms
are differential evolution, whale optimization algorithm, grey wolf optimization, symbiotic
organisms search algorithm, sine-cosine algorithm, and salp swarm algorithm. According to
these algorithms, three variants of the proposed method are developed, in the first variant,
one of the six algorithms will be used to update the current individual based on a predefined
order and the probability of the fitness function for each individual. Whereas, the second
variant updates each individual by permuting the six algorithms, then using the algorithms
in the current permutation to update individuals. The third variant is considered as an ex-
tension of the second variant, which updates all individuals using only one algorithm from
the six algorithms. Three different experiments are carried out using CEC 2014 and CEC
2017 benchmark functions to evaluate the efficiency of the proposed approach. Moreover,
the proposed approach is compared with well known MH methods, including the six meth-
ods used to build it. Comparison results confirmed the efficiency of the proposed approach
compared to other approaches according to different performance measures.
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