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Optimum tie switches allocation and DG placement based on maximisation of system loadability using discrete artificial bee colony algorithm by Aman, M.M., Jasmon, G.B., Mokhlis, Hazlie, Bakar, Ab Halim Abu

“…This study presents a new approach for simultaneous optimum distributed generation (DG) placement and optimum tie-switch allocation based on maximisation of system loadability using discrete artificial bee colony algorithm. …”
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An improved dynamic load balancing for virtualmachines in cloud computing using hybrid bat and bee colony algorithms by Ullah, Arif

“…Therefore, to overcome these problems, this study proposed an improved dynamic load balancing technique known as HBAC algorithm which dynamically allocates task by hybridizing Artificial Bee Colony (ABC) algorithm with Bat algorithm. …”
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Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing by Parmesivan, Yuganes, Hasan, Sazlinah, Muhammed, Abdullah

“…Cloud computing has become biggest buzz in the computer era these days.It runs entire operating systems on the cloud and doeverything on cloud to store data off-site.Cloud computing is primarily based on grid computing, but it’s a new computational model.Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services.The primary substance of cloud computing is to deal the computing power,storage,different sort of stages and services which assigned tothe external users on demand through the internet.Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing.In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example,when cloud clients try to access and send request tothe same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers.Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded.A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time.The proposed hybrid algorithm has been experimented by using CloudSim simulator.The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm.…”
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A krill herd behaviour inspired load balancing of tasks in cloud computing by Hasan, Raed Abdulkareem, Mohammed, Muamer N.

“…The proposed work was aimed at optimizing resource allocation in MCC in an energy-efficient manner. The performance of the suggested Krill-LB was benchmarked against that of Honey Bee Behavior Load Balancing (HBB-LB), Kill Herd, and Round Robin algorithms.…”
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Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

“…The optimal sizing and allocation of distributed generation problem is based on active power loss reduction and voltage profiles improvement. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Prediction of rainfall trends using Mahalanobis-Taguchi system by Muhammad Arieffuddin, Mohd Jamil, Mohd Yazid, Abu, Sri Nur Areena, Mohd Zaini, Nurul Haziyani, Aris, Nur Syafikah, Pinueh, Nur Najmiyah, Jaafar, Wan Zuki Azman, Wan Muhammad, Faizir, Ramlie, Nolia, Harudin, Emelia Sari, ., Nadiatul Adilah, Ahmad Abdul Ghani

“…The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. …”
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Prediction of Rainfall Trends using Mahalanobis-Taguchi System by Jamil M.A.M., Abu M.Y., Zaini S.N.A.M., Aris N.H., Pinueh N.S., Jaafar N.N., Muhammad W.Z.A.W., Ramlie F., Harudin N., Sari E., Ghani N.A.A.A.

“…The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. …”