Abstract
Particle swarm optimization (PSO) has previously been parallelized only by adding more particles to the swarm or by parallelizing the evaluation of the objective function. However, some functions are more efficiently optimized with more iterations and fewer particles. Accordingly, we take inspiration from speculative execution performed in modern processors and propose speculative evaluation in PSO (SEPSO). Future positions of the particles are speculated and evaluated in parallel with current positions, performing two iterations of PSO at once.
We also propose another way of making use of these speculative particles, keeping the best position found instead of the position that PSO actually would have taken. We show that for a number of functions, speculative evaluation gives dramatic improvements over adding additional particles to the swarm.
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References
Belal, M., El-Ghazawi, T.: Parallel models for particle swarm optimizers. Intl. Journal of Intelligent Computing and Information Sciences 4(1), 100–111 (2004)
Schutte, J.F., Reinbolt, J.A., Fregly, B.J., Haftka, R.T., George, A.D.: Parallel global optimization with the particle swarm algorithm. International Journal for Numerical Methods in Engineering 61(13), 2296–2315 (2004)
Mostaghim, S., Branke, J., Schmeck, H.: Multi-objective particle swarm optimization on computer grids. Technical Report 502, AIFB Institute, DEC (2006)
Venter, G., Sobieszczanski-Sobieski, J.: A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. In: Proceedings of the 6th World Congresses of Structural and Multidisciplinary Optimization (2005)
Koh, B.-I., George, A.D., Haftka, R.T., Fregly, B.J.: Parallel asynchronous particle swarm optimization. International Journal of Numerical Methods in Engineering 67, 578–595 (2006)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 120–127 (2007)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: International Conference on Neural Networks IV, Piscataway, NJ, pp. 1942–1948 (1995)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
McNabb, A., Gardner, M., Seppi, K.: An exploration of topologies and communication in large particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 712–719 (May 2009)
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Gardner, M., McNabb, A., Seppi, K. (2010). Speculative Evaluation in Particle Swarm Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_7
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DOI: https://doi.org/10.1007/978-3-642-15871-1_7
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