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...

In many real-world applications, the problem arises that several objectives have to be optimized concurrently leading to a multi-objective optimization problem. Since these goals are typically contradictory, it comes as no surprise that the solution set— the so-called Pareto set—of such problems does (in general) not consist of one single solution. Moreover, under some mild conditions, this set...

Innovization (innovation through optimization) is a relatively new concept in the field of multi-objective engineering design optimization. It involves the use of the Pareto-optimal solutions of a problem, to unveil hidden mathematical relationships between variables, objectives and constraint functions. Since the relationships themselves come from the Pareto-optimal dataset, they can be though...

Semi-active fluid viscous dampers as a subset of control systems have shown their ability to reduce seismic responses of tall buildings. In this paper, multi-objective optimization of the performance of this group of dampers in reducing the seismic responses of buildings is studied using multi-objective genetic algorithms. For numerical example, two 7 and 18 stories buildings are chosen and mod...

The generation of the optimal walking trajectory is an important question for a biped robot to keep walking stably. This paper is proposed for generating the walking trajectories resulted in best performance of the biped robot using multi-objective evolutionary computation. We formulate a trajectory generation problem as a multi-objective optimal problem. We obtain all Pareto-optimal solutions ...

Optimal trajectory planning for robot manipulators is always a hot spot in research fields of robotics. This paper presents two new novel general methods for computing optimal motions of an industrial robot manipulator (STANFORD robot) in presence of obstacles. The problem has a multi-criterion character in which three objective functions, a maximum of 72 variables and 103 constraints are consi...

another, so that it is not possible to improve on any of the objective functions without deteriorating at least one of the other objective functions. This is known as the concept of Pareto optimality (Rao, 1991). In multiobjective optimization, as opposed to single-objective optimization, there may not exist an unambigous optimal solution (global minimum or global maximum). Characteristic of th...

In this paper we present a new interactive procedure for multiObjective optimization, which is based on the use of a set of value functions as a preference model built by an ordinal regression method. The procedure is composed of two alternating stages. In the first stage, a representative sample of solutions from the Pareto optimal set (or from its approximation) is generated. In the second st...