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Multi objective bee colony optimization framework for grid job scheduling
Published 2013“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources.…”
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Bee foraging behaviour techniques for grid scheduling problem
Published 2013“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.These resources are collected together to make a huge computing power.Job scheduling problem is one of the key issues in grid computing and failing to look into grid scheduling results in uncompleted view of the grid computing.Achieving optimized performance of grid system, and matching application requirements with available computing resources, are the objectives of grid job scheduling.Bee colony approaches are more adaptive to grid scheduling due to high heterogeneous and dynamic nature of resources and applications in grid.These algorithms have shown encouraging results in terms of time and cost.This paper presents some resent research activities inspired by bee foraging behavior for grid job scheduling especially ABC and BCO approaches.Different original studies related to this area are briefly described along with their comparisons against them and results.The review summary of their derived algorithms and research efforts is done.…”
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Optimisation of energy efficient hybrid flowshop scheduling problem using firefly algorithm
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A multi-objective Spiral Dynamic algorithm and its application for PD design
Published 2017“…In terms of its application in solving a real problem, both algorithms successfully optimize the PD and control the system very well. …”
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Hybrid genetic manta ray foraging optimization and its application to interval type 2 fuzzy logic control of an inverted pendulum system
Published 2020“…To test the algorithm with a real application, the algorithms were applied to an interval type 2 fuzzy logic controller (IT2FLC) of an inverted pendulum system. …”
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Comparative analysis of sine cosine and social network search-based algorithm for software test redundancy reduction optimization
Published 2024“…More precisely, our work intents to investigate the algorithms' performance in terms of test reduction percentage and execution time. …”
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Reduced Galloping Column Algorithm For Memory Testing
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Fsm-Based Enhanced March C- Algorithm For Memory Built-In Self-Test
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Artificial Bee Colony Algorithm for Pairwise Test Generation
Published 2017“…In a case where PABC is not at its optimal stage or its best performance, the experiments of a test case are effectively competitive. …”
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Pairwise Test Data Generation based on Flower Pollination Algorithm
Published 2017“…Currently, researches on Flower Pollination Algorithm (FPA) are very active and its applications have been proven successes to solve many problems. …”
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Bats echolocation-inspired algorithms for global optimisation problems
Published 2016“…The works related to swarm intelligence algorithms include the development of the algorithm itself, its modification and improvisation as well as its application in solving global optimisation problems. …”
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A fault syndromes simulator for random access memories
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Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing
Published 2023“…Thus, these techniques are unable to detect failures that occur due to the improper use of context data, which is becoming an increasingly common issue in mobile applications nowadays. We enhanced the Q-Learning algorithm for action selection based on potential action abilities and proposed a tool, namely CrashDroid, that allows the automation of testing context-aware Android applications. …”
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Spiral sine-cosine algorithm for global optimization
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