Crystal Energy Optimization Algorithm. (28th November 2014)

• Record Type: Journal Article
• Authors: Feng, Xiang; Ma, Meiyi; Yu, Huiqun
• Is Part Of: Computational intelligence. Volume 32:Number 2(2016)
• Page Start: 284
• Page End: 322
• Publication Date: 2014-11-28
• Subjects: crystal energy optimizer (CEO), computational intelligence, parallel algorithm, nature‐inspired algorithm, traveling salesman problem (TSP); Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12053
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature‐inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self‐organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large‐scale distributed parallel optimization; (2) it can converge and avoid local optimum; and (3) it is flexible and easy to adapt to a wide range of optimization problems.