RAO, SUNIL, MURALI. Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization (Under the direction of Dr. Ranji Ranjithan) In the operations research literature, the Tchebycheff method has been demonstrated to be a useful approach for exploring the non-dominated solutions for multiobjective optimization problems. While this method has been investigated with mathematical pr...

Until recently, optimization was regarded as a discipline of rather theoretical interest, with limited real-life applicability due to the computational or experimental expense involved. Multiobjective optimization was considered as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization....

This thesis proposes a new necessary condition for the infeasibility of non-linear optimization problems (that becomes necessary under convexity assumption) which is stated as a Pareto-criticality condition of an auxiliary multiobjective optimization problem. This condition can be evaluated, in a given problem, using multiobjective optimization algorithms, in a search that either leads to a fea...

In this paper, multiobjective design of multi-machine Power System Stabilizers (PSSs) using Particle Swarm Optimization (PSO) is presented. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electro-mechanical modes of all machines to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions compris...

Title of Document: ONLINE APPROXIMATION ASSISTED MULTIOBJECTIVE OPTIMIZATION WITH HEAT EXCHANGER DESIGN APPLICATIONS Khaled Hassan Mohamed Saleh, Doctor of Philosophy, 2012 Directed By: Shapour Azarm, Professor, Department of Mechanical Engineering Computer simulations can be intensive as is the case in Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The computational cost...

Nature-inspired computational techniques are nowadays being developed for and employed in various application domains. Evolutionary algorithms, known as general and robust optimizers, have recently been extended to deal with multiobjective optimization where search for the best among candidate solutions is performed not according to one, but multiple, usually conflicting objectives. In this pap...

In this work we present a quantum algorithm for multiobjective combinatorial optimization. We show that the adiabatic algorithm of Farhi et al. [arXiv:quant-ph/0001106] can be used by mapping a multiobjective combinatorial optimization problem onto a Hamiltonian using a convex combination among objectives. We present mathematical properties of the eigenspectrum of the associated Hamiltonian and...

This paper considers a capacitated vehicle routing problem with fuzzy random travel time and demand (FRVRP). A chance-constrained multiobjective programming is presented based on fuzzy random theory and converted to a crisp equivalent models under some assumptions. To solve such a multiobjective combinatorial optimization problem, this paper presents a hybrid multiobjective particle swarm optim...

As multiobjective optimization problems have many solutions, evolutionary algorithms have been widely used for complex multiobjective problems instead of simulated annealing. However, simulated annealing also has favorable characteristics in the multimodal search. We developed several simulated annealing schemes for the multiobjective optimization based on this fact. Simulated annealing and evo...

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimizat...