What is it about?

Surrogate-assisted self-accelerated particle swarm optimization (SASA-PSO) is a major modification of an original PSO which uses all previously evaluated particles aiming to increase the computational efficiency.

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Why is it important?

Computational performance of optimization methods and strategies plays a major role in design optimization of engineering problems with multidisciplinary nature, i.e., multidisciplinary design optimization (MDO). This paper introduces a novel approach of surrogate-steered PSO algorithms to internally develop its performance and to automatically make itself more appropriate specially for high-dimensional expensive black-box (HEB) optimization problems.

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This page is a summary of: Surrogate-assisted Self-accelerated Particle Swarm Optimization, January 2014, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2014-1486.
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