scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

Improved Memetic NSGA-II Using a Deep Neighborhood Search

2021 ◽  
Vol 12 (4) ◽  
pp. 138-154
Author(s):  
Samir Mahdi ◽  
Brahim Nini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Pareto Front ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Slow Convergence ◽  
Multi Objective

Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

Multi-Objective Optimization Algorithm Design and Implementation of Mobile Base Station Site Selection

2010 ◽  
Vol 29-32 ◽  
pp. 2496-2502
Author(s):  
Min Wang ◽  
Jun Tang
Keyword(s):  
Site Selection ◽  
Engineering Application ◽  
Optimal Solution ◽  
Base Station ◽  
Pareto Optimal Solution ◽  
Antibody Concentration ◽  
Pareto Optimal ◽  
Memory Cells ◽  
Multi Objective Optimization ◽  
Multi Objective

The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.

Interactive evolutionary multi-objective optimization and decision-making on life-cycle seismic design of bridge

2018 ◽  
Vol 21 (15) ◽  
pp. 2227-2240 ◽  
Author(s):  
Yu-Jing Li ◽  
Hong-Nan Li
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Seismic Design ◽  
Life Cycle Cost ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Multi Objective Optimization ◽  
Seismic Capacity ◽  
Preference Information ◽  
Multi Objective

Considering future seismic risk and life-cycle cost, the life-cycle seismic design of bridge is formulated as a preference-based multi-objective optimization and decision-making problem, in which the conflicting design criteria that minimize life-cycle cost and maximize seismic capacity are treated simultaneously. Specifically, the preference information based on theoretical analysis and engineering judgment is embedded in the optimization procedure. Based on reasonable displacement ductility, the cost preference and safety preference information are used to progressively construct value function, directing the evolutionary multi-objective optimization algorithm’s search to more preferred solutions. The seismic design of a reinforced concrete pier is presented as an application example using the proposed procedure for the global Pareto front corresponding with engineering designers’ preference. The results indicate that the proposed model is available to find the global Pareto front satisfying the corresponding preference and overcoming the difficulties of the traditional multi-objective optimization algorithm in obtaining a full approximation of the entire Pareto optimal front for large-dimensional problems as well as cognitive difficulty in selecting one preferred solution from all these solutions.

Pareto-optimal front for multi-objective optimization of the suspension of a full-vehicle model in the frequency domain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Frequency Domain ◽  
Computational Time ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
Content Type ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Suspension Systems ◽  
Road Profile

PurposeThis study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.Design/methodology/approachUnlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.FindingsThe results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.Originality/valueDespite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

2017 ◽  
Vol 21 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Lu Chen ◽  
◽  
Bin Xin ◽  
Jie Chen ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Normal Vector ◽  
Pareto Optimal ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tangent Hyperplane ◽  
Conflicting Objectives

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

Multi Objective Optimization of Electro Discharge Machining of Resin Bonded Silicon Carbide

2011 ◽  
Vol 110-116 ◽  
pp. 1556-1560
Author(s):  
R. Venkataraman
Keyword(s):  
Silicon Carbide ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Machining Process ◽  
Pareto Optimal ◽  
Electrode Wear ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Electro Discharge Machining ◽  
Pulse On Time

This work is aimed at optimizing the various parameters of the electro discharge machining process in order to Maximize material removal rate (MRR) and Minimize electrode wear rate (EWR) for machining silicon or resin bonded silicon carbide, which is widely used in various applications like high-temperature gas turbines, bearings, seals and linings of industrial furnaces. The five parameters being optimized are intensity supplied by the generator of the EDM machine, open voltage, pulse on time, duty cycle and pressure of flushing fluid. The polynomial models for MRR and EWR proposed by Luis, Puertas and Villa [1] in terms of the five input parameters was used for formation of the objective function. Optimization was carried out using the multi objective genetic algorithm, which is a heuristic search technique that mimics natural selection. A Pareto-optimal front was obtained using this technique, and the points lying on this front represent the set of optimal solutions for the optimization problem. The resultant Pareto– optimal front can be used to select the appropriate operating conditions depending on the specific MRR, EWR or combination requirements.

Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

2013 ◽  
Vol 340 ◽  
pp. 136-140
Author(s):  
Liang You Shu ◽  
Ling Xiao Yang
Keyword(s):  
Optimization Model ◽  
Optimization Problems ◽  
Population Based ◽  
Pareto Optimal ◽  
Order Fulfillment ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Local Search Procedure

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

Sign in / Sign up

Export Citation Format

Share Document