Results 1 to 10 of about 426 (167)

Clustering-Based Monarch Butterfly Optimization for Constrained Optimization [PDF]

International Journal of Computational Intelligence Systems, 2020
Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems. In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications.
Sibo Huang, Han Cui, Xiaohui Wei, Zhaoquan Cai   +3 more
openaire   +3 more sources

Monarch Butterfly Optimization Based Convolutional Neural Network Design [PDF]

Mathematics, 2020
Convolutional neural networks have a broad spectrum of practical applications in computer vision. Currently, much of the data come from images, and it is crucial to have an efficient technique for processing these large amounts of data. Convolutional neural networks have proven to be very successful in tackling image processing tasks.
Nebojsa Bacanin, Timea Bezdan, Eva Tuba, Ivana Strumberger, Milan Tuba   +4 more
openaire   +3 more sources

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Journal of Sensor and Actuator Networks, 2019
Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer ...
Ivana Strumberger, Milan Tuba, Nebojsa Bacanin, Eva Tuba   +3 more
openaire   +3 more sources

A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem [PDF]

Algorithms, 2017
The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration
Shifeng Chen, Rong Chen, Jian Gao
openaire   +4 more sources

Arrhythmia classification based on improved monarch butterfly optimization algorithm

Journal of King Saud University - Computer and Information Sciences, 2022
Heart attack is a major cause of mortality all over the world also heart-related diseases have increased the expenditure of health care. Electrocardiogram (ECG) is one of the simple ways to diagnose heart diseases. Arrhythmia is characterised by abnormalities in the rhythmic pace of heart, which may occur on a random basis in a everyday life of people.
Ashish Nainwal, Yatindra Kumar, Bhola Jha   +2 more
openaire   +3 more sources

Trans-Atlantic Dispersal and Introgression Explain Holarctic Disjunct Distributions in Vanessa Butterflies. [PDF]

Mol Ecol
ABSTRACT Species with disjunct distributions have long puzzled evolutionary biologists and biogeographers. Long‐distance dispersal can play a pivotal role in generating intra‐specific disjunct distributions, initiating early stages of allopatric speciation and leading to eventual interspecific disjunctions.
Palahí A, García-Berro A, Dincă V, Vodă R, Dapporto L, Backström N, Vila R, Pierce NE, Talavera G.   +8 more
europepmc   +2 more sources

Robust approach for optimized path selection in Monarch Butterfly Optimization [PDF]

Journal of University of Shanghai for Science and Technology, 2021
Nature Inspired Computing or (NIC) strives to develop new computing technologies by observing how nature can inspired to solve complex problems under various environmental conditions. This has produced unconventional research in new fields such as neural networks, swarm intelligence, evolutionary computing, and artificial immune systems. NIC technology
Bhanu Sharma, Amar Singh
openaire   +1 more source

Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization

Mathematical Problems in Engineering, 2023
The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting ...
Shixin Huang, Kedao Zhang, Hongmei Li, Xiangjian Chen   +3 more
openaire   +1 more source

Improving monarch butterfly optimization through simulated annealing strategy [PDF]

Journal of Ambient Intelligence and Humanized Computing, 2020
AbstractCurrently, a novel of meta-heuristic algorithm called monarch butterfly optimization (MBO) is presented for solving machine learning and continuous optimization problems. It has been proved experimentally that MBO is superior to artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), Biogeography-based optimization (BBO),
Dongfang Yang, Xitong Wang, Xin Tian, Yonggang Zhang   +3 more
openaire   +1 more source

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population [PDF]

Algorithms, 2018
Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to
Hu, Hui, Cai, Zhaoquan, Hu, Song, Cai, Yingxue, Chen, Jia, Huang, Sibo   +5 more
openaire   +2 more sources