DocumentCode :
1636524
Title :
Adaptive Plan system with Genetic Algorithm using the Variable Neighborhood range Control
Author :
Tooyama, Sousuke ; Hasegawa, Hiroshi
Author_Institution :
Fac. of Syst. Eng., Shibaura Inst. of Technol., Tokyo
fYear :
2009
Firstpage :
846
Lastpage :
853
Abstract :
To improve the calculation cost and the convergence to optimal solutions for multi-peak optimization problems with multiple dimensions, we propose a new evolutionary algorithm, which is an Adaptive plan system with genetic algorithm (APGA). This is an approach that combines the global search ability of a GA and an Adaptive Plan with excellent local search ability. The APGA differs from GAs in how it handles design variable vectors. GAs generally encode design variable vectors into genes, and handle them through GA operations. However, the APGA encodes the control variable vectors of the Adaptive Plan, which searches for local minima, into its genes. The control variable vectors determine the global behavior of the AP, and design variable vectors are handled by the AP in the optimization process of the APGA. In this paper, the Variable Neighborhood range Control (VNC), which changes a neighborhood range based on an individual´s situation-fitness, is introduced into the APGA to dramatically improve the convergence up to the optimal solution. The APGA/VNC is applied to some benchmark functions to evaluate its performance. We confirmed satisfactory performance through these various benchmark tests.
Keywords :
adaptive systems; convergence; genetic algorithms; minimisation; planning (artificial intelligence); search problems; adaptive plan system; convergence; genetic algorithm; global search; local minima; local search; multipeak optimization problem; variable neighborhood range control; Adaptive control; Adaptive systems; Benchmark testing; Control systems; Cost function; Design optimization; Evolutionary computation; Genetic algorithms; Optimal control; Programmable control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-2958-5
Electronic_ISBN :
978-1-4244-2959-2
Type :
conf
DOI :
10.1109/CEC.2009.4983033
Filename :
4983033
Link To Document :
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