Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications

The most existing constrained optimization evolutionary algorithms (COEAs) for solving constrained optimization problems (COPs) only focus on combining a single EA with a single constraint-handling technique (CHT). As a result, the search ability of these algorithms could be limited. Motivated by th...

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Vydané v:Applied Mechanics and Materials Ročník 536-537; číslo Advances in Mechatronics, Robotics and Automation II; s. 476 - 480
Hlavný autor: Long, Wen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Zurich Trans Tech Publications Ltd 01.04.2014
Predmet:
Algorithms
Benchmarking
Computer simulation
Constraints
Evolutionary algorithms
Optimization
Searching
Engineering Application
Constraint-Handling Technique
Knowledge-Base
Evolutionary Algorithm (EA)
Constrained Optimization Problem
ISBN:9783038350781, 3038350788
ISSN:1660-9336, 1662-7482, 1662-7482
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Popis
Shrnutí:The most existing constrained optimization evolutionary algorithms (COEAs) for solving constrained optimization problems (COPs) only focus on combining a single EA with a single constraint-handling technique (CHT). As a result, the search ability of these algorithms could be limited. Motivated by these observations, we propose an ensemble method which combines different style of EA and CHT from the EA knowledge-base and the CHT knowledge-base, respectively. The proposed method uses two EAs and two CHTs. It randomly combines them to generate novel offspring individuals during each generation. Simulations and comparisons based on four benchmark COPs and engineering optimization problem demonstrate the effectiveness of the proposed approach.
Bibliografia:Selected, peer reviewed papers from the 2014 2nd International Conference on Mechatronics, Robotics and Automation, (ICMRA 2014), March 8-9, 2014, Zhuhai, China
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ISBN:9783038350781
3038350788
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.536-537.476