Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization حسب Abu Zitar, Raed

"…Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. …"
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A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation حسب Iman M. Hosseini Naveh (16891482)

"…The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). …"

Iterative Least Squares Functional Networks Classifier حسب Faisal, Kanaan A

"…A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. …"
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From Collatz Conjecture to chaos and hash function حسب Masrat Rasool (17807813)

"…<p>The non-linear property of Chaos is a promising approach to information security, and many accomplishments have been made by combining Chaos with several sub-security domains, including chaos-based stream ciphers, block ciphers, image encryption, and hash functions. Most Chaos-based hash functions are insecure or inefficient due to their dependence on complex, attacked multi-dimensional maps or uncertain, weak one-dimensional maps like logistic and tent. …"

Evolution Of Activation Functions for Neural Architecture Search حسب Nader, Andrew

"…However, to the best of our knowledge, the design of new activation functions has mostly been done by hand. In this work, we propose the use of a self-adaptive evolutionary algorithm that searches for new activation functions using a genetic programming approach, and we compare the performance of the obtained activation functions to ReLU. …"
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Sensitivity to parameters and general applicability of genetic algorithms and simulated annealing algorithms for mapping data to multicomputers. (c1994) حسب Kawash, Jalal Y.

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Salp swarm algorithm: survey, analysis, and new applications حسب Abualigah, Laith

"…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …"
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Radial basis function networks for contingency analysis of bulkpower systems حسب Refaee, J.A.

"…Radial basis function networks (RBFNs) are used for contingency evaluation of bulk power system. …"
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Belief selection in point-based planning algorithms for POMDPs حسب Azar, Danielle

"…Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. …"
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Improving the Secure Socket Layer Protocol by modifying its Authentication function حسب El-Kassar, A. N.

"…It is used to allow the client to identify the server and optionally allow the server to identify the client. The most common cryptographic algorithm used for this function is RSA. …"
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Genetic and heuristic algorithms for regrouping service sites. (c2000) حسب Tabbara, Hiba

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Properties of simulated annealing and genetic algorithms for mapping data to multicomputers حسب Mansour, Nashat

"…We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. …"
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Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis حسب Md. Shafayet Hossain (16888833)

Evolutionary algorithms, simulated annealing and tabu search: a comparative study حسب Youssef, H.

"…The termevolutionary algorithmis used to refer to any probabilistic algorithmwhose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). …"
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Mobile robots obstacles avoidance using dynamic window approach حسب Abubakr, Omer Ali

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A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems حسب Wali Khan Mashwani (14590504)

"…Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. …"

An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection حسب Abu Zitar, Raed

"…The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. …"

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm حسب Saima Hassan (14918003)

"…Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. …"

A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks حسب Saad, Walid

"…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …"
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Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise حسب Saab, Samer S.

"…This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. …"
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