A new reduced gradient method for solving linearly constrained multiobjective optimization problems

Mustapha El Moudden () and Ahmed El Ghali ()
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Mustapha El Moudden: Moulay Ismail University
Ahmed El Ghali: Moulay Ismail University

Computational Optimization and Applications, 2018, vol. 71, issue 3, No 5, 719-741

Abstract: Abstract In this paper, we consider the linearly constrained multiobjective minimization, and we propose a new reduced gradient method for solving this problem. Our approach solves iteratively a convex quadratic optimization subproblem to calculate a suitable descent direction for all the objective functions, and then use a bisection algorithm to find an optimal stepsize along this direction. We prove, under natural assumptions, that the proposed algorithm is well-defined and converges globally to Pareto critical points of the problem. Finally, this algorithm is implemented in the MATLAB environment and comparative results of numerical experiments are reported.

Keywords: Multiobjective optimization; Reduced gradient methods; Pareto critical point; Bisection algorithm; Linear constraints (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-018-0023-1

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