Stopping criteria for MAPLS-AW, a hybrid multi-objective evolutionary algorithm

Evolutionary algorithms are widely used to solve multi-objective optimization problems effectively by performing global search over the solution space to find better solutions. Hybrid evolutionary algorithms have been introduced to enhance the quality of solutions obtained. One such hybrid algorithm...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 20; číslo 6; s. 2409 - 2432
Hlavní autoři: Bhuvana, J., Aravindan, Chandrabose
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Springer Nature B.V
Témata:
Artificial Intelligence
Computational Intelligence
Control
Convergence
Design
Engineering
Evolutionary algorithms
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Mutation
Optimization
Robotics
Searching
Solution space
EELD
Hybrid multi-objective evolutionary algorithms
Stopping criteria
Convergence
ISSN:1432-7643, 1433-7479
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Shrnutí:Evolutionary algorithms are widely used to solve multi-objective optimization problems effectively by performing global search over the solution space to find better solutions. Hybrid evolutionary algorithms have been introduced to enhance the quality of solutions obtained. One such hybrid algorithm is memetic algorithm with preferential local search using adaptive weights (MAPLS-AW) (Bhuvana and Aravindan in Soft Comput, doi: 10.1007/s00500-015-1593-9 , 2015 ). MAPLS-AW, a variant of NSGA-II algorithm, recognizes the elite solutions of the population and preferences are given to them for local search during the evolution. This paper proposes a termination scheme derived from the features of MAPLS-AW. The objective of the proposed scheme is to detect convergence of population without compromising quality of solutions generated by MAPLS-AW. The proposed termination scheme consists of five stopping measures, among which two are newly proposed in this paper to predict the convergence of the population. Experimental study has been carried out to analyze the performance of the proposed termination scheme and to compare with existing termination schemes. Several constrained and unconstrained multi-objective benchmark test problems are used for this comparison. Additionally, a real-time application economic emission and load dispatch has also been used to check the performance of the proposed scheme. The results show that the proposed scheme identifies convergence of population much earlier than the existing stopping schemes without compromising the quality of solutions.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1651-3