Artificial Flora (AF) Optimization Algorithm
Source abstract
Abstract
Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Slime Mold and Myxomycetes Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, Hebei Province, China
School of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning Province, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Hebei Province · Fundamental Research Funds for the Central Universities of China
References (50)
Show source-provided references
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