Levy flights in metaheuristics optimization algorithms–a review
M Chawla, M Duhan - Applied Artificial Intelligence, 2018 - Taylor & Francis
In recent years, Levy flight (LF) is increasingly being employed as search mechanism in
metaheuristics optimization algorithms (MOA) to solve complex real world problems. LF …
metaheuristics optimization algorithms (MOA) to solve complex real world problems. LF …
Deformation prediction based on denoising techniques and ensemble learning algorithms for concrete dams
M Liu, Z Wen, H Su - Expert Systems with Applications, 2024 - Elsevier
Constructing a deformation prediction model for dams that can accurately capture
deformation trends is crucial to ensure their operational safety. The accurate monitoring data …
deformation trends is crucial to ensure their operational safety. The accurate monitoring data …
A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms
IB Salehpoor, S Molla-Alizadeh-Zavardehi - Applied Soft Computing, 2019 - Elsevier
Portfolio selection is a key issue in the business world and financial fields. This article
presents a new decision making method of portfolio optimization (PO) issues in different risk …
presents a new decision making method of portfolio optimization (PO) issues in different risk …
A Q-learning based hyper-heuristic scheduling algorithm with multi-rule selection for sub-assembly in shipbuilding
T Wang, Y Zhang, X Hu - Computers & Industrial Engineering, 2024 - Elsevier
Sub-assembly is the basic stage of ship hull construction. It is necessary to optimize the
scheduling of sub-assembly to shorten its assembly cycle and ensure the normal execution …
scheduling of sub-assembly to shorten its assembly cycle and ensure the normal execution …
A performance-driven multi-algorithm selection strategy for energy consumption optimization of sea-rail intermodal transportation
Various powerful differential evolution (DE) algorithms have been developed in the past
years, although none of them can consistently perform well on all types of problems …
years, although none of them can consistently perform well on all types of problems …
Using meta-learning for multi-target regression
Choosing the most suitable algorithm to perform a machine learning task for a new problem
is a recurrent and complex task. In multi-target regression tasks, when problem …
is a recurrent and complex task. In multi-target regression tasks, when problem …
Designing bijective S-boxes using Algorithm Portfolios with limited time budgets
Substitution boxes (S-boxes) are essential parts of symmetric-key cryptosystems. Designing
S-boxes with satisfactory nonlinearity and autocorrelation properties is a challenging task for …
S-boxes with satisfactory nonlinearity and autocorrelation properties is a challenging task for …
A new hybrid decision making approach for housing suitability map** of an urban area
In urban planning, housing evaluation of residential areas plays a critical role in promoting
economic efficiency. This study produced an evolutionary‐based map through the …
economic efficiency. This study produced an evolutionary‐based map through the …
The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction
MM Mirończuk - Knowledge and Information Systems, 2018 - Springer
The aim of this study is to propose an information extraction system, called BigGrams, which
is able to retrieve relevant and structural information (relevant phrases, keywords) from semi …
is able to retrieve relevant and structural information (relevant phrases, keywords) from semi …
[HTML][HTML] Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation
Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic
stability. The existing yield estimating models exhibit deficiencies in insufficient examination …
stability. The existing yield estimating models exhibit deficiencies in insufficient examination …