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

Moudden, Mustapha El; Ghali, A. El

doi:10.1007/s10589-018-0023-1

Cited by 6 publications

(3 citation statements)

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“…The fundamental idea behind this method is to construct, for each objective function, a quadratic approximation in the vicinity of the actual iteration. Thus, the descent direction is determined by minimizing the maximum of these quadratic models, subject to certain feasibility constraints 31,32 . Indeed, without making any assumptions about the goal functions, the second‐order information can be approximated for each objective function by a shared diagonal matrix at each iteration.…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

“…An inexact line search is then carried out in this direction to determine the next iteration. 31,32 Nesterov's scheme 33 is adopted to accelerate the proposed iterative algorithm. The error analysis was performed to select the best compromise solution that has been determined from the Pareto-optimal front using three quick and reliable decision-making techniques, including TOPSIS, Shannon entropy, and LINMAP methods.…”

Section: Introductionmentioning

confidence: 99%

“…By minimizing the maximum quadratic model close to the current iteration, the common descent direction is found for each objective function. An inexact line search is then carried out in this direction to determine the next iteration 31,32 . Nesterov's scheme 33 is adopted to accelerate the proposed iterative algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Performance optimization of solar‐powered heat engine using a multiobjective accelerated algorithm

Moudden

Hssikou

2023

Heat Trans

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In this study, a multiobjective optimization approach was used to conduct a thermodynamic investigation of a solar Brayton and endoreversible heat engine. The thermo‐economic performance capabilities of such machines with hybrid input power, solar‐fuel, are examined numerically. Throughout this study, three performance indicators of the cycle, including the power output, the thermo‐economic performance function, and the thermal efficiency are optimized concurrently employing a multiobjective steepest descent method, named the Accelerated Diagonal Steepest Descent algorithm. Furthermore, to properly analyze the error, three strategies are employed in the decision‐making step to identify the optimal compromise solution, and the deviation indices under these strategies are analyzed. The numerical experiments reveal that the present algorithm outperforms the two popular multiobjective algorithms: the multiobjective particle swarm optimization method and the elitist nondominated sorting genetic algorithm. The relevance of the presented algorithm with respect to the previous ones is examined by means of a deviation index. Finally, these experiments show the optimal design parameters which lead to the best performance of the heat engine.

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Section: Multiobjective Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%