  This paper proposes a novel population-based meta-heuristic optimization
algorithm, called Perfectionism Search Algorithm (PSA), which is based on the
psychological aspects of perfectionism. The PSA algorithm takes inspiration
from one of the most popular model of perfectionism, which was proposed by
Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are
generated by mimicking different types and aspects of perfectionistic behavior.
In order to have a complete perspective on the performance of PSA, the proposed
algorithm is tested with various nonlinear optimization problems, through
selection of 35 benchmark functions from the literature. The generated
solutions for these problems, were also compared with 11 well-known
meta-heuristics which had been applied to many complex and practical
engineering optimization problems. The obtained results confirm the high
performance of the proposed algorithm in comparison to the other well-known
algorithms.
