Abstract
The particle swarm optimization (PSO) is a stochastic evolutionary computation technique based on the behavior of swarms that can be used to optimize objects with complex search spaces. However, it has been observed that its performance varies duo to the dimensionality of the object and the location of the global optimum in the search space. This paper introduces a “random” velocity boundary condition to address the problem, where the velocity boundary alters randomly to prevent the velocity of a particle from stopping on a same boundary during the evolution. Simulation results on two benchmark functions with 30 and 300 dimensionalities and three types of locations of the global optimum solutions in the search spaces have shown that with the proposed “random” velocity boundary condition, a highly competitive optimization performance can be obtained for PSO regardless of the dimensionality and the location of the global optimum solution.
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References
Kennedy, J., Eberhart, R.: Particle Swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1944–1948. IEEE Computer Society Press, Los Alamitos (1995)
van den Bergh, F., Engelbrecht, A.P.: A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8, 225–239 (2004)
Huang, T., Mohan, A.S.: A Hybrid Boundary Condition for Robust Particle Swarm Optimization. IEEE Antennas and Wireless Propagation Letters 4, 112–117 (2005)
Carlisle, A., Doizier, G.: An off-the-shelf PSO. In: Proceedings of the Workshop on Particle swarm optimization, Indiannapolis, IN, pp. 1–6 (2001)
Eberhart, R., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86 (2001)
Robinson, J., Rahmat-Samii, Y.: Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation 52, 397–407 (2004)
Takahama, T., Sakai, S.: Solving constrained optimization problems by the epsilon constrained particle swarm optimizer with adaptive velocity limit control. In: The 2nd IEEE International Conference of Cybernetics and Intelligent System, pp. 683–689. IEEE Computer Society Press, Los Alamitos (2006)
Ratnaweera, A., Halgamuge, S.: Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Transaction on Evolutionary Computation 8, 240–255 (2004)
Kennedy, J.: Small Worlds and Mega-minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: IEEE Congress on Evolutionary Computation, pp. 1931–1938 (1999)
Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) Evolutionary Programming VII. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Clerc, M., Kennedy, J.: The Particle Swarm-explosion, Stability, and Convergence in a Mutildimensional Complex Space. IEEE Transaction Computation 6, 58–73 (2002)
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Li, J., Ren, B., Wang, C. (2007). A Random Velocity Boundary Condition for Robust Particle Swarm Optimization. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_11
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DOI: https://doi.org/10.1007/978-3-540-74769-7_11
Publisher Name: Springer, Berlin, Heidelberg
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