numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

In deregulated electricity markets, generation companies with the aim of maximum revenue need to provide trading strategies to the electricity trading market, which contributed to a self-scheduling model. When considering the uncertainty of price, the trading strategies are required to maximize the revenue as well as minimizing the risks brought by uncertainties. In this paper, a multi-objectiv...

We develop a novel sensor placement method that maximizes monitoring performance while minimizing deployment cost. Our integrates reduced order model and multi-objective combinatorial optimization. first decompose the spatio-temporal state field to be monitored by proper orthogonal decomposition (POD), we use Gaussian Process uncertainty in each POD mode. Next, lazy greedy (LG)-∊-constraint opt...

Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by overabundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance...

Manufacturing industries aim to improve product quantity and quality. These trade-off objectives are typically manifested as cycle time reduction motion accuracy improvement. Corner smoothing approaches that reduce the of piecewise linear trajectories proposed in literature. This study tackles two by Pareto-optimal corner with constraints imposed kinematic limits, continuity conditions user-spe...

This paper is devoted to a Pareto frontier generation technique, which is aimed at subsequent visualization of the Pareto frontier in an interaction with the user. This technique known as the Interactive Decision Maps technique was initiated about 30 years ago. Now it is applied for decision support in both convex and non-convex decision problems in various fields, from machinery design to envi...

This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework formulates these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest path problem in an auxiliary network. We design tools and techniques for exploiting the network model in order to acce...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed wit...