| Title | An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Gu, Y., Liu, N. |
| Conference Name | 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) |
| Date Published | jun |
| Keywords | Adaptive, adaptive grey wolf algorithm, Adaptive systems, AdGWO algorithm, bacterial foraging algorithm, bacterial foraging optimization algorithm, BFO, competitive algorithm, composability, compositionality, convergence, grey systems, grey wolf optimization, Grey Wolf optimizer, Microorganisms, Optimization, particle swarm optimisation, particle swarm optimization, population system, premature convergence, pubcrawl, Sociology, Statistics, swarm intelligence, swarm intelligence optimization algorithm, Swarm optimization algorithm |
| Abstract | In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm. |
| DOI | 10.1109/ICAICA50127.2020.9182707 |
| Citation Key | gu_adaptive_2020 |
An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm