Chinese Corporate Social Responsibility by Multiple Objective Portfolio Selection and Genetic Algorithms

Qi, Yue; Wu, Fang; Peng, Xiaofeng; Steuer, Ralph E.

doi:10.1002/mcda.1489

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…e.g. [24,25,26,27,28,29]. Population based methods, though by their nature inexact, have gained much popularity in application oriented communities which have no problem with accepting suboptimal solutions in exchange for method generality, versatility and simplicity, allowing easy in-house codings.…”

Section: Related Workmentioning

confidence: 99%

On upper approximations of Pareto fronts

Kaliszewski

Miroforidis

2018

J Glob Optim

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In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing a small but real mechanical problem. Practical implications of the results are discussed.

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Section: Related Workmentioning

confidence: 99%

On upper approximations of Pareto fronts

Kaliszewski

Miroforidis

2018

J Glob Optim

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“…The second approach, based on inexact, mostly population type optimization methods (cf., e.g., [2,[4][5][6]10,[25][26][27]29,30]), aims at producing discrete feasible approximations (lower shells) of Pareto fronts. In the second approach, no guarantee is offered that the resulting approximations include actual elements of the PF.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multiobjective optimization with bounds on objective functions

Kaliszewski

Miroforidis

2020

J Glob Optim

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When solving large-scale multiobjective optimization problems, solvers can get stuck because of memory and/or time limitations. In such cases, one is left with no information on the distance to the best feasible solution, found before the optimization process has stopped, to the true Pareto optimal solution. In this work, we show how to provide such information. To this aim we make use of the concept of lower shells and upper shells, developed in our earlier works. No specific assumptions about the problems to be solved are made. We illustrate the proposed approach on biobjective multidimensional knapsack problems derived from single-objective multidimensional knapsack problems in the Beasley OR Library. We address cases when a top-class commercial mixed-integer linear solver fails to provide Pareto optimal solutions attempted to be derived by scalarization.

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Chinese Corporate Social Responsibility by Multiple Objective Portfolio Selection and Genetic Algorithms

Cited by 2 publications

References 34 publications

On upper approximations of Pareto fronts

On upper approximations of Pareto fronts

Cooperative multiobjective optimization with bounds on objective functions

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