Minimization over randomly selected lines

An International Journal of Optimization and Control: Theories & Applications

article

Ismet Sahin¹ 

[1] National Institute of Standards and Technology

关键词: Random Lines;    nonlinear optimization;    evolutionary optimization;    population-based optimization;    quadratic interpolation;    crossover operator;    mutation operator;    stopping criterion;    Differential Evolution;    Particle Swarm;   

DOI  :  10.11121/ijocta.01.2013.00167

学科分类：地球科学(综合)

来源: Balikesir University

【 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.

【 授权许可】

CC BY