A quantum inspired gravitational search algorithm for numerical function optimization

作     者：Soleimanpour-moghadam, Mohadeseh Nezamabadi-pour, Hossein Farsangi, Malihe M. 

作者机构：Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2014年第267卷

页      面：83-100页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Swarm intelligence Gravitational search algorithm Quantum computing Numerical function optimization 

摘      要：Gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel GSA using quantum mechanics theories to generate a quantum-inspired gravitational search algorithm (QIGSA). The application of quantum mechanics theories in the proposed QIGSA provides a powerful strategy to diversify the algorithm s population and improve its performance in preventing premature convergence to local optima. The simulation results and comparison with nine state-of-the-art algorithms confirm the effectiveness of the QIGSA in solving various benchmark optimization functions.(C) 2013 Elsevier Inc. All rights reserved.