Journal ArticleDOI
An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems
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TLDR
This study introduces an adaptive mutation operator to enhance the performance of the standard NSGA-III algorithm and shows results that indicate that NS GA-III with UC and adaptive mutationoperator outperforms the other NSGA -III algorithms.About:
This article is published in Future Generation Computer Systems.The article was published on 2018-11-01. It has received 152 citations till now. The article focuses on the topics: Crossover & Genetic algorithm.read more
Citations
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Journal ArticleDOI
Behavior of crossover operators in NSGA-III for large-scale optimization problems
Jiao-Hong Yi,Jiao-Hong Yi,Lining Xing,Gai-Ge Wang,Junyu Dong,Athanasios V. Vasilakos,Amir H. Alavi,Ling Wang +7 more
TL;DR: Enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI, and experimental results indicate that the NS GA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
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A Survey of Learning-Based Intelligent Optimization Algorithms
TL;DR: A comprehensive survey of LIOAs is conducted in this paper, which includes statistical analysis about LIOA, classification of L IOA learning method, application of LioAs in complex optimization scenarios, and L IOAs in engineering applications.
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Monarch butterfly optimization: A comprehensive review
TL;DR: This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm including its modifications, hybridizations, variants, and applications.
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Elephant Herding Optimization: Variants, Hybrids, and Applications
TL;DR: Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed.
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Enhancing MOEA/D with information feedback models for large-scale many-objective optimization
TL;DR: This paper proposes six variants of MOEA/D, and these algorithms can be divided into two categories according to the way of selecting individuals whether it is random or fixed, and a new selection strategy has been introduced to further improve the performance of MOE/D-IFM.
References
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Journal ArticleDOI
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
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.
Journal ArticleDOI
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.
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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.
Journal ArticleDOI
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.