Results 21 to 30 of about 41,487 (244)
Firefly Algorithm, Stochastic Test Functions and Design Optimisation [PDF]
, 2010 Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems.
Yang, Xin-She
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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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International Journal of Photoenergy, 2021 A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households ...
Jean De Dieu Niyonteze +7 more
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Engineering Science and Technology, an International Journal, 2023 Metaheuristic optimization algorithms are global optimization approaches that manage the search process to efficiently explore search spaces associated with different optimization problems. Seahorse optimization (SHO) is a novel swarm-based metaheuristic
Feyza Altunbey Özbay
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Metaheuristic Algorithms for Convolution Neural Network [PDF]
, 2016 A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry.
Arymurthy, Aniati Murni +2 more
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A novel metaheuristic optimization algorithm: the monarchy metaheuristic
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2019 In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems.
Ibtissam AHMIA, Méziane AÏDER
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Mathematics, 2021 Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been ...
Md Ashikur Rahman +5 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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Trigonometric words ranking model for spam message classification
IET Networks, EarlyView., 2022 Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when
Suha Mohammed Hadi +7 more
wiley +1 more source
Open Mathematics, 2017 Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a ...
Bansal Shonak +2 more
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