Kang, N., Kokkolaras, M., and Papalambros, P. Y., 2014. "Solving multiobjective optimization problems us- ing quasi-separable mdo formulations and analytical target cascading". Structural and Multidisciplinary Optimization, 50, pp. 849-859....
Optimization techniques are divided into single- and multi-objective [10]. The first type achieves the optimal solution by comparing the utilized objective function. In the case of multi-objective optimization, the goal is to find the POSs [36,37]. Solving multi-objective problems is classified ...
Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of optimums, which constitute the so called ...
Solving Multiobjective Optimization Problems using Evolutionary Algorithm Ruhul Sarker, Hussein A. Abbass, and Charles Newton School of Computer Science, University of New South Wales, ADFA Campus,Northcott Drive, Canberra 2600, Australia, {r.sarker,h.abbass,c.newton}@adfa.edu.au ...
A system and method for solving a set of optimization problems initializes a current region of optimal solutions for the set of optimization problems, performs a reduction phase, and provides the optimal solutions within the current region. The reduction phase creates a random sample of points ...
Carlos A. Coello , Nareli Cruz Corts, Solving Multiobjective Optimization Problems Using an Artificial Immune System, Genetic Programming and Evolvable ... P Risbood,C Nuzman,N Nithi,... - IEEE 被引量: 192发表: 2005年 A Hybrid Evolutionary Immune Algorithm for Multiobjective Optimization Probl...
摘要: In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is...关键词: Multi-objective optimization Water cycle algorithm Pareto-optimal solutions Benchmark ...
Soh et al., 2017] T. Soh, M. Banbara, N. Tamura, and D. Le Berre. Solving Multiobjective Discrete Optimization Problems with Propositional Minimal Model Generation. In International Conference on Principles and Practice of Constraint Programming, pages 596-614. Springer, 2017....
This study presents the modeling of the multiobjective optimization problem in an intuitionistic fuzzy environment. The uncertain parameters are depicted a
Mixed integer linear programming deals with problems where only some of the variables are integers, while other variables can be nonintegers. Finally, nonlinear programming methods are designed to solve optimization problems some of the constraints or objective functions are nonlinear. For a more ...