Search Results - (( variable generating query algorithm ) OR ( evolution optimization bat algorithm ))
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Multi-Swarm bat algorithm
Published 2023“…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”
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Skyline queries on data with uncertain dimensions for efficient computation
Published 2018“…Skyline query is crucial in multi-criteria decision making applications particularly in applications that generate uncertain data. …”
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff
Published 2017“…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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Runtime reduction in optimal multi-query sampling-based motion planning
Published 2023Conference Paper -
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A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. These algorithms generate paths using random numbers and perform efficiently in guiding the robot in known environments. …”
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Keyword query processing interface model of ontological natural language manipulation
Published 2010“…The generated target queries are required to be ranked so that the results are reported in order to their relevance to the user query. …”
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Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation
Published 2017“…An adaptive neuro-fuzzy inference system (ANFIS) was designed which constructs and optimizes a fuzzy logic controller using a given dataset of input/output variables in order for the mobile robot to learn. This depends on the previous outcomes to generate a short path with a low runtime for an obstacle avoidance system unique to mobile robot navigation in dynamic environments. …”
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