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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MetaCluster: An open-source Python library for metaheuristic-based clustering problems
SoftwareX, 2023 Clustering, based on metaheuristic algorithms, is a rapidly developing field. Its goal is to use these methods to reframe clustering issues as optimization problems. In this study, we propose an open-source library named MetaCluster.
Nguyen Van Thieu +2 more
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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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On the use of biased-randomized algorithms for solving non-smooth optimization problems [PDF]
, 2020 Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory ...
Ferrer Biosca, Albert +4 more
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A statistical learning based approach for parameter fine-tuning of metaheuristics [PDF]
, 2016 Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted.
Calvet Liñán, Laura +3 more
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Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 2023 Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these ...
Aydın Sipahioğlu, İslam Altın
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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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A new methodology called dice game optimizer for capacitor placement in distribution systems [PDF]
, 2020 Purpose. Shunt capacitors are installed in power system for compensating reactive power. Therefore, feeder capacity releases, voltage profile improves and power loss reduces.
Al-Haddad, Kamal +5 more
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Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications [PDF]
, 2011 Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints.
A. Chatterjee +23 more
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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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