期刊名称：An International Journal of Optimization and Control: Theories & Applications (IJOCTA)
印刷版ISSN：2146-5703
出版年度：2013
卷号：3
期号：2
页码：111-119
DOI：10.11121/ijocta.01.2013.00167
语种：English
出版社：An International Journal of Optimization and Control: Theories & Applications (IJOCTA)
摘要：This paper presents a population-based evolutionary optimization method for minimizing a given cost function. The mutation operator of this method selects randomly oriented lines in the cost function domain, constructs quadratic functions interpolating the cost function at three different points over each line, and uses extrema of the quadratics as mutated points. The crossover operator modifies each mutated point based on components of two points in population, instead of one point as is usually performed in other evolutionary algorithms. The stopping criterion of this method depends on the number of almost degenerate quadratics. We demonstrate that the proposed method with these mutation and crossover operations achieves faster and more robust convergence than the well-known Differential Evolution and Particle Swarm algorithms.
