Journal Article10.1002/WIDM.43
Evolutionary multiobjective optimization
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TL;DR: This paper presents a very short introduction to multiobjective evolutionary algorithms, including their basic concepts and their main components, including selection mechanisms, diversity maintenance mechanisms, and elitism in a multi‐objective context.
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Abstract: This paper presents a very short introduction to multiobjective evolutionary algorithms, including their basic concepts and their main components. The discussion focuses on algorthmic design and, therefore, the issues discussed include selection mechanisms, diversity maintenance mechanisms, and elitism in a multi-objective context. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 444-447 DOI: 10.1002/widm.43
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Citations
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization
TL;DR: The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems and an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions.
Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization
TL;DR: A new performance indicator, Δp, is defined, which can be viewed as an “averaged Hausdorff distance” between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators generational distance (GD) and inverted generational Distance (IGD).
513
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization
TL;DR: This paper systematically compares PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables to show that PPS is promising for dealing with dynamic environments.
455
Constraint Handling in Multiobjective Evolutionary Optimization
TL;DR: The constraint handling technique is tested on several constrained multiobjective optimization problems and has shown superior results compared to some chosen state-of-the-art designs.
397
Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Optimization
TL;DR: A bi-criterion evolution (BCE) framework of the PC and NPC, which attempts to make use of their strengths and compensates for each other's weaknesses, making it applicable for any non-Pareto-based algorithm.
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.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
8.6K
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Evolutionary algorithms for solving multi-objective problems
Gary B. Lamont,David A. Van Veldhuizen +1 more
- 30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
•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.
5.6K