Recently, a new iterative optimization framework utilizing an evolutionary algorithm called ”Prototype Optimization with Evolved iMprovement Steps” (POEMS) was introduced, which showed good performance on hard optimization problems large instances of TSP and real-valued optimization problems. Especially, on discrete optimization problems such as the TSP the algorithm exhibited much better searc...

Multiobjective combinatorial optimization problems are known to be hard problems for two reasons: their decision versions are often NPcomplete and they are often intractable. Apart from this general observation, are there also variants or cases of multiobjective combinatorial optimization problems which are easy and, if so, what causes them to be easy? This article is a first attempt to provide...

In practical applications it is common to have several conflicting objective functions to optimize. Frequently, these functions are nondifferentiable or discontinuous, could be subject to numerical noise and/or be of black-box type, preventing the use of derivative-based techniques. In this paper we give an overview of some recent developments in Derivative-free Multiobjective Optimization. We ...

This paper deals with multiobjective optimization techniques for a class of hybrid optimal control problems in mechanical systems. We deal with general nonlinear hybrid control systems described by boundary-value problems associated with hybrid-type Euler-Lagrange or Hamilton equations. The variational structure of the corresponding solutions makes it possible to reduce the original “mechanical...

Many water resources systems are characterized by multiple objectives. For multiobjective optimization, typically there can be no single optimal solution which can simultaneously satisfy all the goals, but rather a set of technologically efficient noninferior or Pareto optimal solutions exists. Generating those Pareto optimal solutions is a challenging task and often difficulties arise in using...

J. Deon Garrett. Ph.D. The University of Memphis. February, 2008. Multiobjective Fitness Landscape Analysis and the Design of Effective Memetic Algorithms. Major Professor: Dipankar Dasgupta, Ph.D. For a wide variety of combinatorial optimization problems, no efficient algorithms exist to exactly solve the problem unless P=NP. For these problems, metaheuristics have come to dominate the landsca...

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savi...

In this paper a multiobjective optimal design method of interior permanent magnet synchronous motor ( IPMSM) for traction applications so as to maximize average torque and to minimize torque ripple has been presented. Based on train motion equations and physical properties of train, desired specifications such as steady state speed, rated output power, acceleration time and rated speed of tract...

Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous Multiobjective Optimization Problems(MOPs) is a piecewise continuous ( 1) m D   manifold(where m is the number of objectives). One hand, the traditional Multiobjective Optimization Algorithms(EMOAs) cannot utilize this regularity property; on the other han...

In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in form of an aggregate function which combines individual objectives into a single utility value. As a result, the optimization problem becomes single objective, prior to optimization. Up to 300 parameters must be optimized satisfying objectives which are often competing. We use...