Open AccessMultiobjective Evaluation of Coevolution among Innovation Populations Based on Lotka–Volterra Equilibrium
Author(s) -
Shengyuan Wang,
Wan-Ming Chen,
Rong Wang,
Xiaolan Wu
Publication year - 2021
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2021/5569108
Subject(s) - computer science , mathematical optimization , coevolution , volterra equations , sensitivity (control systems) , stability (learning theory) , process (computing) , construct (python library) , multi objective optimization , management science , ecology , mathematics , economics , machine learning , nonlinear system , physics , quantum mechanics , electronic engineering , engineering , biology , programming language , operating system
The collaborative evaluation of enterprise innovation populations is a hot issue. The Lotka–Volterra model is a mature method used to evaluate the interaction mechanism of populations and is widely used in innovation ecology research studies. The Lotka–Volterra model mainly focuses on the quantitative characteristics of the interactive populations. The growth mechanisms cannot explain all the synergy mechanisms of the innovative populations. The collaborative evaluation between enterprise innovation populations is a typical multiobjective evaluation problem. The multichoice goal programming model is a mature method to solve multiobjective optimization problems. This paper combines the Lotka–Volterra model and multichoice goal programming method to construct a three-stage multiobjective collaboration evaluation method based on Lotka–Volterra equilibrium. An evaluation example is used to illustrate the application process of this method. The method proposed in this paper has excellent performance in computing, parameter sensitivity analysis, and model stability analysis.