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.

翻译：本文提出一种新颖的基于种群的元启发式优化算法，称为完美主义搜索算法（PSA），该算法基于完美主义的心理学特性。PSA算法灵感来源于Hewitt与Flett提出的最流行的完美主义模型之一。在PSA算法的每次迭代中，通过模拟不同类型和方面的完美主义行为来生成新解。为全面评估PSA的性能，本文选取文献中35个基准函数，将所提算法应用于各类非线性优化问题测试。这些问题的求解结果还与11种已应用于众多复杂工程优化问题的知名元启发式算法进行了比较。所得结果证实了所提算法相较于其他知名算法具有更高的性能。