Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling

2007 ◽  
pp. 803-817 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Udaya Bhaskara Rao N. ◽  
S. Karthik
Keyword(s):  
Decision Making ◽  
Thermal Power ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Power Scheduling

Parameter Based Multi-Objective Optimization of Video CODECs

2011 ◽  
pp. 288-308
Author(s):  
F. Al-Abri ◽  
E.A. Edirisinghe ◽  
C. Grecos
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Formulation ◽  
Video Codec ◽  
Video Content ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Video Codecs ◽  
Multi Objective ◽  
On Demand

This chapter presents a generalised framework for multi-objective optimisation of video CODECs for use in off-line, on-demand applications. In particular, an optimization scheme is proposed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment, which minimises codec complexity and video distortion. The encoding/decoding parameters that have a significant impact on the performance of the codec are initially obtained through experimental analysis. A mathematical formulation by means of regression is subsequently used to associate these parameters with the relevant objectives and define a Multi-Objective Optimization (MOO) problem. Solutions to the optimization problem are reached through a Non-dominated Sorting Genetic Algorithm (NSGA-II). It is shown that the proposed framework is flexible on the number of objectives that can jointly be optimized. Furthermore, any of the objectives can be included as constraints depending on the requirements of the services to be supported. Practical use of the proposed framework is described using a case study that involves video content transmission to a mobile hand.

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

2012 ◽  
pp. 44-66
Author(s):  
M. Kanthababu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Manufacturing Processes ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Local Optima ◽  
Multi Objective ◽  
Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

scholarly journals A Multi-Objective Decision-Making Framework for Transportation Investments

2010 ◽  
Author(s):  
Mashrur Chowdhury ◽  
Pulin Tan
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
Discrete Problem ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Multi Objective ◽  
Transportation Investment ◽  
Continuous Problems ◽  
Decision Making Framework

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a pre-defined set of alternatives is available, whereas continuous problems are not characterized by a pre-defined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study.

scholarly journals Efficient multi-objective optimization methods for computationally intensive urban water resources models

2014 ◽  
Vol 17 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Mohammad Mortazavi-Naeini ◽  
George Kuczera ◽  
Lijie Cui
Keyword(s):  
Ant Colony Optimization ◽  
Water Resource ◽  
Optimization Methods ◽  
Optimization Method ◽  
Ant Colony ◽  
Urban Water ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

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