Abstract
To solve complex global optimization problems, Artificial Physics Optimization (APO) algorithm is presented based on Physicomimetics framework, which is a population-based stochastic algorithm inspired by physical force. The solutions (particles) sampled from the feasible region of the problems are treated as physical individuals. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. Driven by virtual force, the individuals move towards others with bigger masses, which is an analogy of the particles flying towards the better fitness region. To easily analyze the algorithm, a vector model of APO algorithm is constructed. Based on the vector model, APO algorithm can performs well in diversity if some conditions can be satisfied.
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References
Shah-Hosseini, H.: The Intelligent Water Drops Algorithm: a Nature-Inspired Swarm-based Optimization Algorithm. Int. J. Bio-Inspired Computation 1(1/2), 71–79 (2009)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of ICNN 1995 – IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE CS Press, Perth (1995)
Formato, R.: Central Force Optimization: a New Nature Inspired Computational Framework for Multidimensional Search and Optimization. In: Nature Inspired Cooperative Strategies for Optimization (NICSO), vol. 129, pp. 221–238 (2008)
Birbil, S., Fang, S.: An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimozation 25(3), 263–282 (2003)
Rocha, A., Fernandes, E.: On Charge Effects to the Electromagnetism-like Algorithm. In: The 20th International Conference, EURO Mini Conference “Continuous Optimization and Knowledge-Based Technologies” (EurOPT 2008), Vilnius Gediminas Technical University Publishing House “Technika” (2008)
Spears, W., Spears, D., Heil, R., Kerr, W., Hettiarachchi, S.: An Overview of Physicomimetics. In: LNCS-State of the Art Series, vol. 3324, pp. 84–97 (2005)
Spears, W., Heil, R., Zarzhitsky, D.: Artificial Physics for Mobile Robot Formations. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2287–2292 (2005)
Spears, D., Kerr, W., Spears, W.: Physics-Based Robots Swarms for Coverage Problems. International Journal on Intelligent Control and Systems 11(3), 11–23 (2006)
Kerr, W., Spears, D., Spears, W., et al.: Two Formal Gas Models for Multi-agent Sweeping and Obstacle Avoidance. In: Hinchey, M.G., Rash, J.L., Truszkowski, W.F., Rouff, C.A. (eds.) FAABS 2004. LNCS (LNAI), vol. 3228, pp. 111–130. Springer, Heidelberg (2004)
Spears, W., Spears, D.: Using Artificial Physics to Control Agents. In: IEEE International Conference on Information, Intelligence, and Systems, Washington, DC, pp. 281–288 (1999)
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Xie, L., Zeng, J., Cui, Z. (2009). The Vector Model of Artificial Physics Optimization Algorithm for Global Optimization Problems. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_74
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DOI: https://doi.org/10.1007/978-3-642-04394-9_74
Publisher Name: Springer, Berlin, Heidelberg
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