Book Chapter10.1016/B978-0-12-398512-5.00005-0
Chapter 5 – Multiobjective Optimization and Advanced Topics
Kuang-Hua Chang
- 01 Jan 2015
pp 325-406
49
TL;DR: An area of multiple-criteria decision-making, concerning mathematical optimization problems involving more than one objective functions to be optimized simultaneously, where optimal decisions need to be taken in the presence of trade-offs between two or more objectives that may be in conflict.
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Abstract: Multiobjective optimization (also known as multiobjective programming, vector optimization, multicriteria optimization, multiattribute optimization, or Pareto optimization) is an area of multiple-criteria decision-making, concerning mathematical optimization problems involving more than one objective functions to be optimized simultaneously Multiobjective optimization has been applied to many fields of science, including engineering, where optimal decisions need to be taken in the presence of trade-offs between two or more objectives that may be in conflict Indeed, in many practical engineering applications, designers are making decisions between conflict objectives—for example, maximizing performance while minimizing fuel consumption and emission of pollutants of a vehicle In these cases, a multiobjective optimization study should be performed, which provides multiple solutions representing the trade-offs among the objective functions
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TOPSIS decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
Máximo Méndez,Mariano Frutos,Fabio Maximiliano Miguel,Ricardo Aguasca-Colomo +3 more
- 20 Nov 2020
TL;DR: A two-stage methodology is proposed: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM.
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas,Kalyanmoy Deb +1 more
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
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•Book
Nonlinear Multiobjective Optimization
Kaisa Miettinen
- 26 Sep 2011
TL;DR: This paper is concerned with the development of methods for dealing with the role of symbols in the interpretation of semantics.
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Survey of multi-objective optimization methods for engineering
R.T. Marler,Jasbir S. Arora +1 more
TL;DR: A survey of current continuous nonlinear multi-objective optimization concepts and methods finds that no single approach is superior and depends on the type of information provided in the problem, the user's preferences, the solution requirements, and the availability of software.
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