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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| Published in: | Soft computing (Berlin, Germany) Vol. 20; no. 6; pp. 2409 - 2432 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
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01.06.2016
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Aravindan, Chandrabose Bhuvana, J. |
| Author_xml | – sequence: 1 givenname: J. surname: Bhuvana fullname: Bhuvana, J. email: bhuvanaj@ssn.edu.in organization: Department of Computer Science and Engineering, SSN College of Engineering – sequence: 2 givenname: Chandrabose surname: Aravindan fullname: Aravindan, Chandrabose organization: Department of Computer Science and Engineering, SSN College of Engineering |
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| SubjectTerms | 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 |
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| Title | Stopping criteria for MAPLS-AW, a hybrid multi-objective evolutionary algorithm |
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