Journal ArticleDOI
Multiobjective cuckoo search for design optimization
Xin-She Yang,Suash Deb +1 more
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TLDR
A new cuckoo search for multiobjective optimization is formulated and applied to solve structural design problems such as beam design and disc brake design.About:
This article is published in Computers & Operations Research.The article was published on 2013-06-01. It has received 729 citations till now. The article focuses on the topics: Metaheuristic & Cuckoo search.read more
Citations
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Journal ArticleDOI
Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization
TL;DR: Simulation results indicate that the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters.
Book ChapterDOI
Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications
TL;DR: Cuckoo Search (CS) is an optimization technique, developed in 2009, is a highly efficient algorithm as discussed by the authors, it is an algorithm that is based on population and is also a nature-inspired metaheuristic algorithm, which is easy to implement for such applications.
Book ChapterDOI
Cuckoo Search Algorithm for Parameter Identification of Fermentation Process Model
TL;DR: The promising metaheuristic algorithm Cuckoo search (CS) has been adapted and applied for a first time to a parameter identification of S. cerevisiae fed-batch fermentation process model and results confirm the effectiveness and efficacy of the applied CS algorithm.
Journal ArticleDOI
Heuristic-driven strategy for boosting aerial photography with multi-UAV-aided Internet-of-Things platforms
TL;DR: In this article , a hybrid multi-objective heuristic coalescing two relevant concepts of stochastic optimization is proposed to solve the problem of deploying a fleet of UAVs equipped with rotating gimbal-mounted cameras over large-scaled terrains.
Journal ArticleDOI
Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction
TL;DR: In this article , a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and stochastic configuration network (SCN) is proposed.
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.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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.
Journal ArticleDOI
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.