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About 10,000 papers for “Optimization algorithm”
George A. Silver · 1979
An abbreviated simplex algorithm suitable for the programmable pocket calculator is applied to chemical equilibrium problems, including the problem of locating the inflection point in the potentiometric titration of plutonium.
Cited by 7Open access
Liberta, gy · Figshare · 2022
This method will be used to solve complex nonlinear dynamic problems, such as complex controller parameter tuning.
Cited by 0Open access
Augusto Luis Ballardini · 2016
K-Means is one of the most popular clustering algorithms, and it is easy to implement It seeks to minimize the sum of squared errors with an iterative optimization At every iteration, it moves the data centroids toward the closer cluster until no point can move anymore Drawbacks It implements a Hill-climbing procedure Highly dependent on the choice of K Sensitive to initialization: how do we choose the initial partitions? K-means and drawbacks
Cited by 0
D. Karthika Renuka, Pg Scholar · 2016
In recent days, Electronic mail system is a store and forward mechanism used for the purpose of exchanging documents across computer network through Internet. Spam is an unwanted mail which contains unsolicited and harmful data that are irrelevant to the specified users. In the proposed system, the spam classification is implemented using Naive Bayes classifier, which is a probabilistic classifier based on conditional probability applicable for more complex classification problems. Implementation of feature selection using hybrid Ant Colony Optimization serves to be more efficient which gives good results for the above system that has been proposed in this paper.
Cited by 0
Huiyuan Chen (1939633), Feixiong Cheng (151564), Jing Li (10611) · Figshare · 2020
<p>The details of the optimization algorithm for solving <b>U</b><sup>(<i>i</i>)</sup> and <b>V</b><sup>(<i>i</i>)</sup>, as well as its correctness and convergence results can be found here.</p> <p>(PDF)</p>
Cited by 0Open access
George A. Tsianos (2818714), Lisa N. MacFadden (2818711) · Figshare · 2016
<p>A genetic algorithm was used to compute the muscle excitation parameters that satisfied the performance criteria. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.s002" target="_blank">S2 Appendix</a> for a detailed description of each step.</p>
Cited by 0Open access
Liberta, gy · Figshare · 2022
This method will be used to solve complex nonlinear dynamic problems, such as complex controller parameter tuning.
Cited by 0Open access
Sasha Goldshtein, Dima Zurbalev, Ido Flatow · Apress eBooks · 2012
At the heart of some applications lie specialized algorithms designed for a specific domain and based on assumptions that do not hold universally. Other applications rely on well-tested algorithms that fi t many domains and have been relevant for decades in the entire fi eld of computer software. We believe that every software developer can benefit and obtain insight from some of the crown jewels of algorithms research, as well as the algorithm categories on which software frameworks are based. Although some parts of this chapter might be somewhat difficult if you do not have a strong mathematical background, they are well worth the effort.KeywordsShort PathMinimal Span TreeTuring MachineEdit DistanceShort PathThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Cited by 4
Eva Trojovská, Mohammad Dehghani, Pavel Trojovský · IEEE Access · 2022
In this paper, a new bio-inspired metaheuristic algorithm called Zebra Optimization Algorithm (ZOA) is developed; its fundamental inspiration is the behavior of zebras in nature. ZOA simulates the foraging behavior of zebras and their defense strategy against predators’ attacks. The ZOA steps are described and then mathematically modeled. ZOA performance in optimization is evaluated on sixty-eight benchmark functions, including unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC2015, and CEC2017. The results obtained from ZOA are compared with the performance of nine well-known algorithms. The simulation results show that ZOA can solve optimization problems by creating a suitable balance between exploration and exploitation and has a superior performance compared to nine competitor algorithms. ZOA’s ability to solve real-world problems has been tested on four engineering design problems, namely, tension/compression spring, welded beam, speed reducer, and pressure vessel. The optimization results show that ZOA is an effective optimizer in determining the values of the design variables of these problems compared to the nine competitor algorithms.
Cited by 463Open access
Mohammad Hussein Amiri, Nastaran Mehrabi Hashjin, Mohsen Montazeri et al. · Research Square · 2023
Abstract The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 132 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both local search and exploitation, as well as in global search and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. The performance of the HO consistently surpassed that of the top 3 algorithms in achieving optimal value, except for 29 functions. However, although it did not exhibit strong convergence in these 29 functions, the standard deviation for them was lower than the other investigated algorithms, illustrating its ability to manage the functions effectively. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The Wilcoxon signed test demonstrates that HO exhibits a notable and statistically significant advantage over the investigated algorithms in effectively addressing the optimization problems examined in this study.
Cited by 17Open access
Mirjalili, Seyedali, Amiri, Mohammad Hussein, Mehrabi Hashjin, Nastaran et al. · Torrens University · 2024
The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho.
Cited by 0Open access
Mohammad Hussein Amiri, Nastaran Mehrabi Hashjin, Mohsen Montazeri et al. · Scientific Reports · 2024
The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
Cited by 539Open access
Mohammad Yassami, Payam Ashtari · Multimedia Tools and Applications · 2023
Cited by 17
Fouad, Ahmed · 2019
Cited by 0Open access
Fouad, Ahmed · 2017
Cited by 0Open access
Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2025
This paper proposes a heuristic optimization algorithm (BOA) based on the behavioral characteristics of bluebirds. This algorithm fully simulates the bluebird's song strategy, dynamic territory selection, foraging priorities, and group collaboration and information transmission mechanisms in its natural environment. It formally describes individual search behavior, global search strategy, and information network interactions through a mathematical model. The algorithm innovatively incorporates a dynamic song-driven mechanism, adaptive territory migration, a multi-level foraging strategy, and an information network sharing mechanism, achieving a nature-inspired global optimization search. This paper conducts an in-depth theoretical analysis and mathematical modeling of the algorithm, demonstrating its applicability and potential for continuous optimization problems. This provides new insights into the research of optimization algorithms based on ecological behavior.
Cited by 0Open access
Alexis Bénichou (19832361), Jean-Baptiste Masson (107354), Christian L. Vestergaard (14559912) · Figshare · 2024
<p>(A) Illustration of a single step of the greedy stochastic algorithm. The putative compression Δ<i>L</i>(<i>G</i>, <i>θ</i>, <i>s</i>) that would be obtained by contracting each of the subgraphs in the minibatch is calculated, and the subgraph contraction resulting in the highest compression is selected (highlighted in blue). (B) Example of motif set inferred in the connectome of the right hemisphere of the mushroom bodies (MB right) of the <i>Drosophila</i> larva. (C) Evolution of the codelength during a single algorithm run. The algorithm is continued until no more subgraphs can be contracted. The representation <i>θ</i>* = <i>θ</i><sub><i>t</i></sub> with the shortest codelength is selected; here, after the 31st iteration (indicated by a vertical black dashed line). The horizontal orange dashed line indicates the codelength of the corresponding simple graph model without motifs (see Motif-free reference codes). (D) The algorithm is run a hundred times for each dyadic base model and the most compressing model is selected. Histograms represent the codelengths of models with motifs after each run of the greedy algorithm; colors correspond to the different base models (blue: ER model, orange: configuration model, pink: reciprocal ER model, yellow: reciprocal configuration model, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012460#pcbi.1012460.g001" target="_blank">Fig 1B</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012460#pcbi.1012460.t001" target="_blank">Table 1</a>); vertical dashed lines represent the codelengths of models without motifs, and the black dashed line indicates the codelength of the shortest-codelength model—here the configuration model with motifs.</p>
Cited by 0Open access
Rebecca-Monica Pui, Pavel-Antoniu Martin · Zenodo (CERN European Organization for Nuclear Research) · 2023
All Rights Reserved. All material appearing on the this article and the idea (“content”) is protected by copyright under U.S. Copyright laws/ Canadian Copyright/ EU Copyright laws/ Berne Convention 1988 and is the property of authors or the party credited as the provider of the content. You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of database. You may not alter or remove any copyright or other notice from copies of the content on this article. Copying or storing any content except as provided above is expressly prohibited without prior written permission of the author or the copyright holder identified in the individual content’s copyright notice. For permission to use the content, please contact the authors. Under Canadian Crown Copyright License: Copyright is an author/creator's exclusive legal right to reproduce, publish and sell a work. Abstract. The use of cutting-edge technologies has revolutionized the way political marketing strategies are formulated. By optimizing the political environment through advanced tools, it is now possible to create effective and inclusive strategies that cater to the needs of diverse groups. With the help of new technologies, it is possible to analyze vast amounts of data available on the internet and compile it based on various factors such as time stamp, gender, age, and orientation. This information can be used to generate insights into the preferences of different groups, which can help in creating targeted political marketing strategies. In essence, the use of new technologies in political marketing has made it possible to create data-driven and inclusive strategies that cater to the needs of diverse groups. By leveraging these technologies, politicians can connect with their constituents in a more meaningful way and create a positive impact on the political landscape.
Cited by 0Open access
M Blanco-rey, K Heinz, P L De Andres · 2008
Cited by 0
Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
With the increasing complexity of modern engineering optimization problems, traditional optimization algorithms often get stuck in local optima when dealing with high-dimensional, multimodal, and non-convex problems. Animal behavior in nature provides rich inspiration for optimization algorithms, especially the marsupial anteater (Tamandua), whose flexible predation behavior and strong adaptability to the environment offer new ideas for continuous optimization problems. This paper proposes an optimization algorithm based on the behavior of the marsupial anteater, named the Tamandua Optimization Algorithm (TOA). Inspired by the marsupial anteater's predation strategy, global migration behavior, and energy allocation mechanism, this algorithm achieves efficient optimization search through a combination of multi-scale olfactory perception, local fine-grained search and global exploration, historical trajectory memory, and group collaborative information flow. This paper provides a detailed mathematical description of the algorithm modeling, iterative update mechanism, search strategy, and energy adjustment, providing new theoretical methods and application foundations for optimization problems.
Cited by 0Open access