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Algorithms 2017, 10(4), 119; doi:10.3390/a10040119

An Optimization Algorithm Inspired by the Phase Transition Phenomenon for Global Optimization Problems with Continuous Variables

1,2
and
1,*
1
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2
School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China
*
Author to whom correspondence should be addressed.
Received: 8 September 2017 / Revised: 29 September 2017 / Accepted: 9 October 2017 / Published: 20 October 2017
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Abstract

In this paper, we propose a novel nature-inspired meta-heuristic algorithm for continuous global optimization, named the phase transition-based optimization algorithm (PTBO). It mimics three completely different kinds of motion characteristics of elements in three different phases, which are the unstable phase, the meta-stable phase, and the stable phase. Three corresponding operators, which are the stochastic operator of the unstable phase, the shrinkage operator in the meta-stable phase, and the vibration operator of the stable phase, are designed in the proposed algorithm. In PTBO, the three different phases of elements dynamically execute different search tasks according to their phase in each generation. It makes it such that PTBO not only has a wide range of exploration capabilities, but also has the ability to quickly exploit them. Numerical experiments are carried out on twenty-eight functions of the CEC 2013 benchmark suite. The simulation results demonstrate its better performance compared with that of other state-of-the-art optimization algorithms. View Full-Text
Keywords: phase transition; nature-inspired; continuous optimization; global optimization phase transition; nature-inspired; continuous optimization; global optimization
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Cao, Z.; Wang, L. An Optimization Algorithm Inspired by the Phase Transition Phenomenon for Global Optimization Problems with Continuous Variables. Algorithms 2017, 10, 119.

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