An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems
In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
Review of nature inspired metaheuristic algorithm selection for combinatorial t-way testing
The metaheuristic algorithm is a very important area of research that continuously improves
in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm …
in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm …
[HTML][HTML] Energy management and power quality improvement of microgrid system through modified water wave optimization
A comparative study of energy management strategies and PQ improvement schemes for a
Fuel Cell, Battery, and SuperCapacitor integrated Microgrid system has been projected …
Fuel Cell, Battery, and SuperCapacitor integrated Microgrid system has been projected …
A bio-inspired method for mathematical optimization inspired by arachnida salticidade
This paper proposes a new meta-heuristic called Jum** Spider Optimization Algorithm
(JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the …
(JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the …
A learning automata-based hybrid MPA and JS algorithm for numerical optimization problems and its application on data clustering
S Barshandeh, R Dana, P Eskandarian - Knowledge-Based Systems, 2022 - Elsevier
Nature-inspired meta-heuristic algorithms possess various actions inspired by natural
phenomena, animal behaviors, chemistry or physics laws etc. The actions are utilized to …
phenomena, animal behaviors, chemistry or physics laws etc. The actions are utilized to …
Exploring meta-heuristics for partitional clustering: methods, metrics, datasets, and challenges
Partitional clustering is a type of clustering that can organize the data into non-overlap**
groups or clusters. This technique has diverse applications across the different various …
groups or clusters. This technique has diverse applications across the different various …
Bicriteria single-machine scheduling with multiple job classes and customer orders
Customer order scheduling problems focus on completing all jobs of the same order
consecutively to reduce the holding costs. Multiple job-class scheduling problems involving …
consecutively to reduce the holding costs. Multiple job-class scheduling problems involving …
Blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered‐based energy efficient routing protocol in wireless sensor network
In this manuscript, a blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered
based energy efficient routing protocol in wireless sensor network (WSN) is proposed for …
based energy efficient routing protocol in wireless sensor network (WSN) is proposed for …
[HTML][HTML] MetaCluster: An open-source Python library for metaheuristic-based clustering problems
Clustering, based on metaheuristic algorithms, is a rapidly develo** field. Its goal is to use
these methods to reframe clustering issues as optimization problems. In this study, we …
these methods to reframe clustering issues as optimization problems. In this study, we …