Search Results - (( _ distribution swarm algorithm ) OR ( peer evaluation ((a algorithm) OR (bat algorithm)) ))

An improved bat algorithm with artificial neural networks for classification problems by Rehman Gillani, Syed Muhammad Zubair

“…Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. …”
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Levy tunicate swarm algorithm for solving numerical and real-world optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

“…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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Bats echolocation-inspired algorithms for global optimisation problems by Nafrizuan, Mat Yahya

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An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization by Majid, Mad Helmi Ab.

“…Searching a source with a complex spatial distribution pattern is one of the possible swarm robotics tasks. …”
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A Preliminary Study on Camera Auto Calibration Problem Using Bat Algorithm by Mohd Annuar, Khalil Azha, Selamat, Nur Asmiza, Jaafar, Hazriq Izzuan, Mohamad, Syahrul Hisham

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A study on the parameter selection of bat algorithm in in optimizing parameters in camera auto calibration problem by Mohd Said, Rahaini, A Aziz, Khairul Azha, Zainal Abidin, Amar Faiz, Mat Jizat, Jessnor Arif, Mohd Khairuddin, Ismail, Widiyanto, Sigit, Abdul Waduth, Mohamed Faisal

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Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm by ,, Dwi Pebrianti, Ann Ayop azmi, Nurnajmi Qasrina, Bayuaji, Luhur, Suarin, Nur Aisyah Syafinaz, ,, Muhammad Syafrullah

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Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

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Improved Bat Algorithm for faster convergence in solving optimisation problem by Ramli, Mohamad Raziff

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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

“…Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. …”
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Bat algorithm for dam–reservoir operation by Ethteram, Mohammad, Mousavi, Sayed Farhad, Karami, Hojat, Farzin, Saeed, Deo, Ravinesh, Othman, Faridah, Chau, Kwok Wing, Sarkamaryan, Saeed, Singh, Vijay P., El-Shafie, Ahmed

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture by Ibrahim, Abeer A.Z., Hashim, Fazirulhisyam, Sali, Aduwati, Noordin, Nor K., Navaie, Keivan, Fadul, Saber M.E.

“…A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. …”
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Improving Vector Evaluated Particle Swarm Optimisation using Multiple Nondominated Leaders by Faradila, Naim, Kian, Sheng Lim, Salinda, Buyamin, Anita, Ahmad, Mohd Ibrahim, Shapiai, Marizan, Mubin, Dong, Hwa Kim

“…The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. …”
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Optimum MV Feeder Routing and Substation siting and rating in Distribution Network by Hasan, Ihsan Jabbar, Ab Ghani, Mohd Ruddin, Gan, Chin Kim

“…This paper proposes an evolutionary algorithm to determine the optimum distribution substation placement and sizing by using the particle swarm optimization algorithm and optimum feeder routing using modified minimum spanning tree algorithm. …”
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Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization by Pradyumna Kumar Mohapatra, Saroja Kumar Rout, Sukant Kishoro Bisoy, Sandeep Kautish, Muzaffar Hamzah, Muhammed Basheer Jasser, Ali Wagdy Mohamed

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Two objectives big data task scheduling using swarm intelligence in cloud computing by Diallo, Laouratou, Hassan Abdalla Hashim, Aisha, Olanrewaju, Rashidah Funke, Islam, Shayla, Zarir, Abdullah Ahmad

“…In this direction, this paper first gives review of some previous scheduling algorithms used in cloud. Then, it proposes a type of swarm intelligence called Particle Swarm Optimization (PSO) algorithm to diminish cost though meeting deadlines. …”
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Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters by Islam, J., Meraj, S.T., Masaoud, A., Mahmud, M.A., Nazir, A., Kabir, M.A., Hossain, M.M., Mumtaz, F.

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Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters by Islam, J., Meraj, S.T., Masaoud, A., Mahmud, M.A., Nazir, A., Kabir, M.A., Hossain, M.M., Mumtaz, F.

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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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