Evolutionary Game Theory in Multi-Objective Optimization Problem
Author(s) -
Maozhu Jin,
Xia Lei,
Jian Du
Publication year - 2010
Publication title -
international journal of computational intelligence systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.385
H-Index - 41
eISSN - 1875-6891
pISSN - 1875-6883
DOI - 10.1080/18756891.2010.9727754
Subject(s) - mathematical optimization , computer science , optimization problem , convergence (economics) , game theory , evolutionary algorithm , process (computing) , mathematics , mathematical economics , economics , economic growth , operating system
Multi-objective optimization focuses on simultaneous optimization of multiple targets. Evolutionary game theory transforms the optimization problem into game strategic problem and using adaptable dynamic game evolution process intelligently obtains the optimized strategy. The problem of multiple frequency offsets estimation in distributed multiple inputs and multiple outputs system is real-world multi-objective search and optimization problems which are naturally posed as non-linear programming problems having multiple objectives. Simulation results evidence the proposed algorithm is superior to other algorithms with more robust convergence and environmental applicability.
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