Vol.41, No.3, 2022, pp.1143-1155, doi:10.32604/csse.2022.021604
N-Body Simulation Inspired by Metaheuristics Optimization
  • Muhammad Ali Ismail*, Maria Waqas, Farah Sadiq
National Center in Big Data and Cloud Computing, Department of Computer and Info Systems Engineering, NED University of Engineering and Technology, Karachi, Pakistan
* Corresponding Author: Muhammad Ali Ismail. Email:
Received 08 July 2021; Accepted 12 August 2021; Issue published 10 November 2021
The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.
N-body problem; metaheuristics optimization; particle swarm optimization; heavenly bodies
Cite This Article
Ismail, M. A., Waqas, M., Sadiq, F. (2022). N-Body Simulation Inspired by Metaheuristics Optimization. Computer Systems Science and Engineering, 41(3), 1143–1155.
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