The paper describes a multiobjective optimization study for industrial styrene reactors using non-dominated sorting genetic algorithm (NSGA). Several twoand threeobjective functions, namely, production, yield and selectivity of styrene, are considered for adiabatic as well as steam-injected styrene reactors. Pareto optimal (a set of equally good) solutions are obtained due to conflicting effect...

This paper presents a solution for the Class Responsibility Assignment Case of the 2016 Transformation Tool Contest. The task is to assign features (methods and attributes with dependencies to each other) to classes and optimize a software metric called CRA-Index. The solution utilizes the rule-based design space exploration framework Viatra-DSE with the Non-dominated Sorting Genetic Algorithm ...

In this paper, a fractional order (FO) PID controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing ...

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...

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. Multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multipl...

It is a known fact that the performance of optimization algorithms for NP-Hard problems vary from instance to instance. We observed the same trend when we comprehensively studied multiobjective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in a construction project. In this paper, instead of using a single algorithm to solve DTCTP, ...

NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

in this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the p-mmrcpsp while due dates are included i...

The huge demand for real time services in wireless mesh networks (WMN) creates many challenging issues for providing quality of service (QoS). Designing of QoS routing protocols, which optimize the multiple objectives is computationally intractable. This paper proposes a new model for routing in WMN by using Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II). The objectives which ar...

—In this paper, we present a modeling and simulation framework for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-perceptron artificial neural network (NN) is trained by a non-dominated sorting genetic algorithm–II (NSGA-II) to forecast point-values and prediction intervals (PIs) of the wind power and load. The output of...