Over the past few years, researchers have developed a number of multi-objective evolutionary algorithms (MOEAs). Although most studies concentrated on solving unconstrained optimization problems, there exists a few studies where MOEAs have been extended to solve constrained optimization problems. As the constraint handling MOEAs gets popular, there is a need for developing test problems which c...

....... The fast and elitist non-dominated sorting genetic algorithm (NSGA-II) contains a mechanism to sort individuals in a multi-objective optimization problem into non-dominated fronts, based on their performance in each optimization variable. When dealing with a bi-objective problem it is possible to carry-out the non-dominated sorting more efficiently, using the new sorting method presente...

This paper presents the multi-objective evolutionary optimization of three-dimensional geometry represented via constructive solid geometry (CSG), a binary tree of boolean operations of solid primitives. NSGA-II is extended for binary tree chromosomes with customized crossover and mutation operators tailored for the evolution of CSG trees and applied for two-objective shape optimization of indo...

A new optimization and design strategy, multi-objective optimization, is applied to improve the performance of Simulated Moving Bed (SMB) and its modification, the Varicol systems with or without reactions. The capabilities of the new approach are illustrated considering three different applications of SMB (and Varicol process), namely, chiral drug separation, production of high concentrated fr...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

In this paper an accurate method is presented for determining of the device sizes in a RF circuit based on genetic algorithm (GA). HSPICE RF simulation is used for evaluating of the fitness of the circuit specifications per every iteration of the GA. Also an example for a LNA is presented for evaluating of non-dominated sorting genetic algorithm (NSGA-II) as a method of multi objective genetic ...

in this paper, experimentally derived correlations of heat transfer and pressure drop are used in a pareto based multi-objective optimization (moo) approach to find the best possible combinations of heat transfer and pressure drop of tio2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. in this study there are four independent design variables: t...

-In this paper, a fast dynamical multi-objective evolutionary algorithm (DMOEA) based of the principle of the minimal free energy in thermodynamics, was developed to solve mechanical component design problems. Its performance was compared with the ε -constraint method and NSGA-II proposed by Deb et al. Simulation results show that the proposed evolutionary approach produces excellent solutions ...

Multi-objective problems with parameter interactions can present difficulties to many optimization algorithms. We have investigated the behaviour of Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II (Non-dominated So...

A device for electrostatic separation of triboelectrically charged plastic particles is modeled and optimized. Electric field in the system is solved numerically by a fully adaptive higher-order finite element method. The movement of particles in the device is determined by an adaptive Runge-Kutta-Fehlberg method. The shape optimization of the electrodes is carried out by a technique based on g...