Genghis Khan shark optimizer: a novel nature-inspired algorithm for engineering optimization
G Hu, Y Guo, G Wei, L Abualigah - Advanced Engineering Informatics, 2023 - Elsevier
This study tenders a new nature-inspired metaheuristic algorithm (MA) based on the
behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for …
behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for …
A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study
J Too, S Mirjalili - Knowledge-Based Systems, 2021 - Elsevier
The rapid expansion of information science has caused the issue of “the curse of
dimensionality”, which will negatively affect the performance of the machine learning model …
dimensionality”, which will negatively affect the performance of the machine learning model …
Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization …
This research presents a new model for finding optimal conditions in the concrete
technology area. To do that, results of a series of laboratory investigations on concrete …
technology area. To do that, results of a series of laboratory investigations on concrete …
Optimization of SVR functions for flyrock evaluation in mine blasting operations
J Huang, J Xue - Environmental Earth Sciences, 2022 - Springer
This study introduces a new model to determine the critical flyrock event in mines. The
flyrock was predicted and optimized using a field database including six parameters and …
flyrock was predicted and optimized using a field database including six parameters and …
Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization
This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended
for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued …
for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued …
Binary moth search algorithm for discounted {0-1} knapsack problem
The discounted {0-1} knapsack problem (DKP) extends the classical 0-1 knapsack problem
(0-1 KP) in which a set of item groups is included and each group consists of three items …
(0-1 KP) in which a set of item groups is included and each group consists of three items …
A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches
High level architecture (HLA) is a software-architecture specification of a distributed
simulation system which does not involve load balancing. As a result, problems of long …
simulation system which does not involve load balancing. As a result, problems of long …
[PDF][PDF] An improved bald eagle search algorithm with Cauchy mutation and adaptive weight factor for engineering optimization
ABSTRACT The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic
algorithm. The algorithm simulates the hunting behavior of eagles, and obtains an optimal …
algorithm. The algorithm simulates the hunting behavior of eagles, and obtains an optimal …
Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem
Monarch butterfly optimization (MBO) has become an effective optimization technique for
function optimization and combinatorial optimization. In this paper, a generalized opposition …
function optimization and combinatorial optimization. In this paper, a generalized opposition …
A novel human learning optimization algorithm with Bayesian inference learning
P Zhang, L Wang, Z Fei, L Wei, M Fei… - Knowledge-Based …, 2023 - Elsevier
Humans perform Bayesian inference in a wide variety of tasks, which can help people make
selection decisions effectively and therefore enhances learning efficiency and accuracy …
selection decisions effectively and therefore enhances learning efficiency and accuracy …