Abstract
Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.
Highlights
Metaheuristic algorithms have been attracting more and more attention for addressing various optimization problems [1]–[3]
Biology-based algorithms are inspired from natural evolution and biological behaviors, which consist of swarm-based algorithms and evolution-based algorithms
For Gravitational search algorithm (GSA)-CNO, chaotic neural oscillators can maintain a great value of gravitational constant in a long period
Summary
YIRUI WANG 1, SHANGCE GAO 1, (Senior Member, IEEE), YANG YU 1, ZIQIAN WANG1, JIUJUN CHENG 2, AND YUKI TODO 3.
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