Performance Evaluation of the Nature Inspired Salp Swarm Algorithm on CEC 2017 Benchmark Problems
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Keywords:
Optimization, Algorithm, Salp, CEC 2017, Meta-heuristic Techniques, OptimalAbstract
Computational optimization algorithms are named according to the number of objectives which
are single-objective; multi-objective and many-objective optimization algorithms. In addition, these
algorithms can be classified according to their design differences. Single objective optimization algorithms
can be classified into two categories; nature inspired and evolutionary algorithms. The nature inspired
optimization algorithms design from observations on the nature especially behavior of the animal swarms.
In literature there are many algorithms have been proposing to solve single objective optimization
problems. Among many nature-inspired algorithms recently an algorithm called Salp Swarm Algorithm
(SSA) is proposed. To evaluate the performance of this algorithm on a challenging problem; in this work,
the effectiveness of the algorithm is evaluated with CEC 2017 benchmark functions. The obtained solutions
are compared with other algorithms on the literature to clearly demonstrate the performance of SSA.
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References
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