A quantum inspired gravitational search algorithm for numerical function optimization

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• 作者：Mohadeseh Soleimanpour-moghadam ; Hossein Nezamabadi-pour ; ^{nezam@mail.uk.ac.ir" class="auth_mail} ; Malihe M. Farsangi
• 关键词：Swarm intelligence ; Gravitational search algorithm ; Quantum computing ; Numerical function optimization
• 刊名：Information Sciences
• 出版年：20 May, 2014
• 年：2014
• 卷：267
• 期：Complete
• 页码：83-100
• 全文大小：1994 K

文摘

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鈥檚 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.