A sequential quadratically constrained quadratic programming technique for a multi-objective optimization problem

In this article a line search algorithm is proposed for solving constrained multi-objective optimization problems. At every iteration of the proposed method, a subproblem is formulated using quadratic approximation of all functions. A feasible descent direction is obtained as a solution of this subp...

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Bibliographic Details
Published in:Engineering optimization Vol. 51; no. 1; pp. 22 - 41
Main Authors: Ansary, Md. Abu Talhamainuddin, Panda, Geetanjali
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 02.01.2019
Taylor & Francis Ltd
Subjects:
Algorithms
Constraints
efficient solution
Multi-objective optimization
Multiple objective analysis
Optimization
Penalty function
purity metric
Quadratic programming
Search algorithms
spread metric
ISSN:0305-215X, 1029-0273
Online Access:Get full text
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Summary:In this article a line search algorithm is proposed for solving constrained multi-objective optimization problems. At every iteration of the proposed method, a subproblem is formulated using quadratic approximation of all functions. A feasible descent direction is obtained as a solution of this subproblem. This scheme takes care some ideas of the sequential quadratically constrained quadratic programming technique for single objective optimization problems. A non-differentiable penalty function is used to restrict constraint violations at every iterating point. Convergence of the scheme is justified under the Slater constraint qualification along with some reasonable assumptions. The proposed algorithm is verified and compared with existing methods with a set of test problems. It is observed that this algorithm provides better results in most of the test problems.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2018.1437154