Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to ac...

To solve single and multi-objective optimization problems, evolutionary algorithms have been created. We use the non-dominated sorting genetic algorithm (NSGA-II) to find Pareto front in a two-objective portfolio query, its extended variant NSGA-III three-objective problem, this article. Furthermore, both we quantify Karush-Kuhn-Tucker Proximity Measure (KKTPM) for each generation determine how...

using new approaches to optimize the process of power transmission line routing can solve many complex problems which power transmission line routing decision-makers are faced. due to the expansion of involved parameters we can consider multi-objective evolutionary algorithms as appropriate method in this area. in this thesis with using multi-objective evolutionary algorithms nsga-ii and offere...

distribution centers (dcs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.this paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. an evolutionary algorithm named non-dominated sorting ant colony optimization (nsaco) is used as the optimi...

Modern this paper proposes non dominated sorting genetic algorithm (NSGA-II) which has feature of adaptive crowding distance for finding optimal location and sizing of Static Var Compensators (SVC) in order to minimize real power losses and voltage deviation and also to improve voltage profile of a power system at the same time. While finding the optimal location and size of SVC, single line ou...

This paper presents an application of elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. This scheduling problem is a bi-objective problem considering two objectives. The first objective is minimization of makespan and the second one being the minimization of flowtime. As a multi-objectiv...

Abstract. This paper examines the effect of crossover operations on the performance of EMO algorithms through computational experiments on knapsack problems and flowshop scheduling problems using the NSGA-II algorithm. We focus on the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. First we show the necessity of crossover operations t...

NSGA-II and its contemporary EMO algorithms were found to be vulnerable in solving many-objective optimization problems having four or more objectives. It is not surprising that EMO researchers have been concentrating in developing efficient algorithms for manyobjective optimization problems. Recently, authors suggested an extension of NSGA-II (NSGA-III) which is based on the supply of a set of...

Many control problems involve simultaneous optimization of multiple performance measures that are often noncommensurable and competing with each other. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theory is applied to obtain the solution of such problems using different scalar approaches. The presence of multiple objectives in a problem usua...

Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...