Surrogate-assisted Self-accelerated Particle Swarm Optimization
Kambiz Haji Hajikolaei, Amir Safari, Gaofeng Wang, Hirpa Gelgele Lemu · 2014
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. A newly in-house developed metamodeling approach named high dimensional model representation with principal component analysis (PCAHDMR), which was specifically established for so called high-dimensional, expensive, blackbox (HEB) problems, is used to approximate a function using all particles calculated during the optimization process. Then, based on the minimum of the constructed metamodel, a term called “metamodeling acceleration” is added to the velocity update formula in the original PSO algorithm. The proposed optimization algorithm performance is investigated using several benchmark problems with different number of variables and the results are also compared with original PSO results. Preliminary results show a considerable performance improvement in terms of number of function evaluations as well as achieved global optimum specifically for high-dimensional problems.