Author:
Filippi Gianluca,Vasile Massimiliano
Abstract
AbstractThis paper proposes a method for the solution of constrained min-max problems. The method is tested on a benchmark of representative problems presenting different structures for the objective function and the constraints. The particular min-max problem addressed in this paper finds application in optimisation under uncertainty when the constraints need to be satisfied for all possible realisations of the uncertain quantities. Hence, the algorithm proposed in this paper search for solutions that minimise the worst possible outcome for the objective function due to the uncertainty while satisfying the constraint functions in all possible scenarios. A constraint relaxation and a scalarisation procedure are also introduced to trade-off between objective optimality and constraint satisfaction when no feasible solutions can be found.
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Control and Optimization,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Software
Reference42 articles.
1. Abdelbar AM, Ragab S, Mitri S (2003) Applying Co-Evolutionary Particle Swam Optimization to the Egyptian Board Game Seega. Proceedings of the First Asian-Pacific Workshop on Genetic Programming, pp 9–15
2. Agnew D (1981) Improved minimax optimization for circuit design. IEEE Trans Circuits Syst 28:791–803
3. Aissi H, Bazgan C, Vanderpooten D (2008) Min-max and min-max regret versions of combinatorial optimization problems: a survey. Eur J Op Res 197:427–438
4. Barbosa HJ (1999) A coevolutionary genetic algorithm for constrained optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol 3, pp 1605–1611
5. Baxter R, Hastings N, Law A, Glass EJ (2008) Algorithms for worst-case design and applications to risk management, vol 39
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献