Abstract
Differential Evolution (DE) is a simple and effective stochastic algorithm for optimization problems, and it became much more popular in recent year because of its easy-implementation and excellent performance. Nevertheless, the performance of DE algorithm is greatly affected by the trial vector generation strategy including mutation strategy and parameter control, both of which still exist some weaknesses, e.g. the premature convergence to some local optima of a mutation strategy and the misleading interaction among control parameters. Therefore in this paper, a novel Di-DE algorithm is proposed to tackle these weaknesses. A depth information based external archive was advanced in our novel mutation strategy, which can get a better perception of the landscape of objective in an optimization. Moreover, a novel grouping strategy was also employed in Di-DE and parameters were updated separately so as to avoid the misleading among parameters. Moreover, a cooperative strategy for information interchange was also advanced aiming at improving the efficiency of the exploration behavior. By absorbing these advancements, the novel Di-DE algorithm can secure better performance in comparison with other famous optimization algorithms. The algorithm validation was conducted on CEC2013 and CEC2017 test suites, and the results revealed the competitiveness of our Di-DE algorithm in comparison with those famous optimization algorithms including Particle Swarm Optimization (PSO) variants, QUasi-Affine TRansformation Evolution (QUATRE) variants and Differential Evolution (DE) variants.
Highlights
There are a lot of optimization demands arising in industry production, intelligent manufacturing, automatic control and other fields nowadays [1], [2] with the development of science and technology, and algorithms tackling those optimization problems can be divided into different categories such as analytical methods and iterative methods
We proposed a depth information based mutation strategy for numerical optimization, and the depth information was extracted from the historical individuals which were recorded in the external archive
Can be extracted and employed in the guidance of the evolution, which may draw the population out of some local optima while maintaining a better convergence speed. The employment of this archive as well as the depth information obtained from the archive can be incorporated into the mutation strategy which determines the search scope of each step in Differential Evolution (DE)
Summary
There are a lot of optimization demands arising in industry production, intelligent manufacturing, automatic control and other fields nowadays [1], [2] with the development of science and technology, and algorithms tackling those optimization problems can be divided into different categories such as analytical methods and iterative methods. Feed back information of the evolution was very useful to guide the adaptations of control parameters, and algorithms employed this thought in the evolution were all classified into this category, e.g. jDE [29], SaDE [30], JADE [31] and SHADE [32] etc All these DE variants had the same feature that they employed the fixed population size in the evolution, and the paper we focused on the performance improvement of DE with fixed population size. We proposed a novel DE variant, named Depth information based Differential Evolution with adaptive parameter control for numerical optimization (Di-DE).
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