APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

“…The mathematical model was transformed into a fitness function and a solution was provided with the Tabu Search Algorithm and Simulated Annealing. …”

Compare the test functions. by Yanqiu Li (683621)

Shift test functions. by Yong Gao (29929)

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Comparison of results of different algorithms. by Zhongzhong Guo (12133402)

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Values of the fitness function for different algorithms run on various companies’ datasets. by Bitanu Chatterjee (15295261)

Convergence results of different dimensional functions under different algorithms. by Guilian Feng (18530806)

Data_Sheet_1_Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10).doc by Gourhari Jana (4015469)

“…<p>Particle Swarm Optimization (PSO), a population based technique for stochastic search in a multidimensional space, has so far been employed successfully for solving a variety of optimization problems including many multifaceted problems, where other popular methods like steepest descent, gradient descent, conjugate gradient, Newton method, etc. do not give satisfactory results. Herein, we propose a modified PSO algorithm for unbiased global minima search by integrating with density functional theory which turns out to be superior to the other evolutionary methods such as simulated annealing, basin hopping and genetic algorithm. …”

Data_Sheet_2_Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10).doc by Gourhari Jana (4015469)

“…<p>Particle Swarm Optimization (PSO), a population based technique for stochastic search in a multidimensional space, has so far been employed successfully for solving a variety of optimization problems including many multifaceted problems, where other popular methods like steepest descent, gradient descent, conjugate gradient, Newton method, etc. do not give satisfactory results. Herein, we propose a modified PSO algorithm for unbiased global minima search by integrating with density functional theory which turns out to be superior to the other evolutionary methods such as simulated annealing, basin hopping and genetic algorithm. …”

Algorithm control flow chart. by Guangliang Li (194029)

Flow chart of this algorithm. by Xuan Chen (158617)

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Qualitative results for the F12 function. by Dingming Yang (8505990)

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Details of multi-modal test functions. by Dingming Yang (8505990)

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Qualitative results for the F14 function. by Dingming Yang (8505990)

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Qualitative results for the F15 function. by Dingming Yang (8505990)

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Qualitative results for the F13 function. by Dingming Yang (8505990)

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Details of fixed-dimension test functions. by Dingming Yang (8505990)

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Algorithm pseudocode. by Cheng-jie Chen (22272090)

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