Search Results - (( evolution optimisation based algorithm ) OR ( data application server algorithm ))
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Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design
Published 2014“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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A competitive co-evolutionary approach for the nurse scheduling problem
Published 2026“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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A technique of dispatching algorithms for web-server cluster
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Mobile application design for protecting the data in cloud using enhanced technique of encryption
Published 2024journal::journal article -
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Load balancing and server consolidation in cloud computing environments: a meta-study
Published 2019“…Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. …”
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Data pre-processing on web server logs for generalized association rules mining algorithm
Published 2008“…Since the pre-processing is tedious process, it depending on the algorithm and purposes of the applications.…”
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Locust- inspired meta-heuristic algorithm for optimising cloud computing performance
Published 2023“…The cloud paradigm has arisen due to a rapid growth in applications and data sizes. Even though cloud computing servers and resources may seem unlimited, this is not true, as increased server usage leads to increased energy consumption and carbon emissions. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…In addition, the system is developed using the developing tools that are essential in web applications, namely. web server, database server, system server and web programming tools such as ASP and Visual Basic. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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On protecting data storage in mobile cloud computing paradigm
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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system
Published 2011“…It can improve the performance and the reliability of a database application. In addition, it can be considered as a data backup method in case of hardware failure, software corruption or even a natural disaster. …”
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Smart grid: Bio-inspired algorithms energy distributions for data centers
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Enhancing Secure Sockets Layer Bulk Data Trnsfer Phase Performance With Parallel Cryptography Algorithm
Published 2007“…The server calculates the MAC of the data the same time the encryption processes the data. …”
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A framework of rough cultural algorithms in optimizing mobile web caching performance
Published 2007“…The common cause of poor performance is due to the direct access to the server (e.g. pitiable performance of server-side applications or during burst crowds) and network infrastructure (e.g. long geographical distances, network overloads, etc.). …”
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