Results 31 to 40 of about 57,703 (300)
Multi-Objective Big Data Optimization with jMetal and Spark [PDF]
, 2017 Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi ...
A Cabanas-Abascal +11 more
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Parallel Hybrid Island Metaheuristic Algorithm
IEEE Access, 2022 This study introduces a novel Parallel Hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base.
Jiawei Li, Tad Gonsalves
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The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms [PDF]
, 2020 open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms.
Caraffini, Fabio, Iacca, Giovani
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Metaheuristics in nature-inspired algorithms [PDF]
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 To many people, the terms nature-inspired algorithm and metaheuristic are interchangeable. However, this contemporary usage is not consistent with the original meaning of the term metaheuristic, which referred to something closer to a design pattern than to an algorithm.
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An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems [PDF]
Journal of Advances in Computer Engineering and Technology, 2020 Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near ...
narges jafari +1 more
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Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters
International Journal of Photoenergy, 2021 The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms.
Daniel T. Cotfas +3 more
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Efficiency Analysis of Swarm Intelligence and Randomization Techniques [PDF]
, 2012 Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark.
Yang, Xin-She
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A Hybrid Algorithm for Metaheuristic Optimization
CoRR, 2019 We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess.
Sujit Pramod Khanna +1 more
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Complexity, 2020 For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm.
Pisut Pongchairerks
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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms
IEEE Access, 2020 Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the ...
Jorge Daniel Mello-Roman +1 more
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