Journal ArticleDOI
A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms
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TLDR
The applicability and efficiency of the proposed hybrid approach in providing the optimal values of all parameters of the evolutionary optimization algorithms to optimize their performance in solving an optimization problem is demonstrated.About:
This article is published in Measurement.The article was published on 2018-01-01. It has received 32 citations till now. The article focuses on the topics: Meta-optimization & Evolutionary algorithm.read more
Citations
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Journal ArticleDOI
Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing
Azadeh Gholami,Hossein Bonakdari,Isa Ebtehaj,Majid Mohammadian,Bahram Gharabaghi,Saeed Reza Khodashenas +5 more
TL;DR: The results show that the ANFIS-PSO/GA model has less uncertainty in different hydraulic conditions of channels for predicting the vertical level of threshold bank profile and can predict satisfactorily the channel bank profiles.
Journal ArticleDOI
Parameter tuning for meta-heuristics
TL;DR: A generalized strategy to find the most suitable value of any parameter for a meta-heuristic algorithm based on the relation between algorithm’s performance and functional landscape is proposed.
Journal ArticleDOI
Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm
TL;DR: A multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models to maximize environmental, technical, and economic benefits by minimizing various objective functions subject to different power system constraints and uncertainty of renewable energy sources is offered.
Journal ArticleDOI
Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network
TL;DR: In this paper, the ultrasonic assisted stir-casting technique improves the uniform dispersion of nano-reinforcements in aluminum hybrid metal matrix composites, and the process parameters of the process are analyzed.
Journal ArticleDOI
An optimization algorithm guided by a machine learning approach
Erik Cuevas,Jorge Gálvez +1 more
TL;DR: This paper introduces an evolutionary optimization algorithm in which knowledge extracted during its operation is employed to guide its search strategy and improves the convergence to difficult high multi-modal optima by using a reduced number of function evaluations.
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
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
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.
Book ChapterDOI
Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey
TL;DR: In this article, the authors survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory, and indicate some problems for future research and include a selective bibliography.