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- W1535622669 abstract "In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems." @default.
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- W1535622669 date "2007-06-20" @default.
- W1535622669 modified "2023-09-27" @default.
- W1535622669 title "Triggered Memory-Based Swarm Optimization in Dynamic Environments" @default.
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- W1535622669 doi "https://doi.org/10.1007/978-3-540-71805-5_70" @default.
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