For multi-objective optimal reactive power dispatch (MORPD), a new approach is proposed where simultaneous minimization of the active power transmission loss, the bus voltage deviation and the voltage stability index of a power system are achieved. Optimal settings of continuous and discrete control variables (e.g. generator voltages, tap positions of tap changing transformers and the number of...

Design of an optimal controller requires the optimization of differential evolution performance measures that are often no commensurable and competing with each other. Being a population based approach; Differential Evolution (DE) is well suited to solve designing problem of TCSC – based controller. This paper investigates the application of DE-based multi-objective optimization technique for t...

Optimal control of water resource systems plays an important role in sustainable development of a nation. In general, most of the water management problems are multi-objective in nature and often involve different types of complexities in their model formulation. Obtaining optimal solutions to such problems is always a challenging task. In multi-objective environment, in order to perceive the e...

Many robotic tasks require solutions that maximize multiple performance objectives. For example, in path-planning, these objectives often include finding short paths that avoid risk and maximize the information obtained by the robot. Although there exist many algorithms for multi-objective optimization, few of these algorithms apply directly to robotic path-planning and fewer still are capable ...

this paper presents the results of a comprehensive study on the efficiency of planar parallel mechanisms, considering their kinetostatic performance and also, their workspace. this aim is approached upon proceeding single- and multi-objective optimization procedures. kinetostatic performances of ten different planar parallel mechanisms are analyzed by resorting to a recent index, kinematic sens...

k-median and k-center are two well-known problems in facility location which play an important role in operation research, management science, clustering and computational geometry. To the best of our knowledge, although these problems have lots of applications, they have never been studied together simultaneously as a multi objective optimization problem. Multi-objective optimization has been ...

We propose a new algorithm of computation using particle swarm in order to solve multi-objective problems more quickly and e ectively. This approach, called accelerated multi-objective particle swarm, is partially based on our previous work [4] and incorporates a vector function as objective function and it uses matrix computation to develop the Pareto front. Unlike all these studies which use ...

In this paper,we present a newpopulation-basedMonte Carlomethod, so-calledMOMCMC (Multi-Objective Markov Chain Monte Carlo), for sampling in the presence of multiple objective functions in real parameter space. The MOMCMC method is designed to address the ‘‘multi-objective sampling’’ problem, which is not only of interest in exploring diversified solutions at the Pareto optimal front in the fun...

Many real-world optimization problems have multiple objectives that have to be optimized simultaneously. In multi-objective optimization problems, the major challenge is to find the set of solutions that achieve the best compromise with regard to the whole set of objectives. Although a great deal of effort has been devoted to solving multi-objective optimization problems, the problem is still o...

When modeling an activated sludge system of a wastewater treatment plant (WWTP), several conflicting objectives may arise. The proposed formulation is a highly constrained bi-objective problem where the minimization of the investment and operation costs and the maximization of the quality of the effluent are simultaneously optimized. These two conflicting objectives give rise to a set of Pareto...