In the present article, the fundamentals of particle swarm optimization (PSO) are reviewed and illustrated by means of examples both taken from standard mathematical optimization and more chemically-oriented. Swarm-based algorithms take their inspiration from collective behavior of social animals and translate these concepts to solve optimization problem. Among this family, particle swarm optimization (PSO) describes the set of candidate solutions to the optimization problem as a swarm of particles moving across the search space along trajectories, governed by their own and neighbors’ best performances.
1.26 - Particle Swarm Optimization / Marini, F.; Walczak, B.. - (2020), pp. 649-666. [10.1016/B978-0-12-409547-2.14581-0].
1.26 - Particle Swarm Optimization
Marini F.
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2020
Abstract
In the present article, the fundamentals of particle swarm optimization (PSO) are reviewed and illustrated by means of examples both taken from standard mathematical optimization and more chemically-oriented. Swarm-based algorithms take their inspiration from collective behavior of social animals and translate these concepts to solve optimization problem. Among this family, particle swarm optimization (PSO) describes the set of candidate solutions to the optimization problem as a swarm of particles moving across the search space along trajectories, governed by their own and neighbors’ best performances.| File | Dimensione | Formato | |
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