Search Results - (( simulation optimization sensor algorithm ) OR ( evolution optimization new algorithm ))
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1
A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising
Published 2015“…The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. …”
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2
Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage
Published 2019“…Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. …”
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3
IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks
Published 2024“…Additionally, simulation results confirm that the proposed algorithm IWDSA exhibits outstanding performance compared to other algorithms utilizing optimization techniques, including genetic algorithms, bat algorithms, ant colony optimization, and swarm optimization. …”
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4
Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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5
Mobile robot path optimization algorithm using vector calculus and mapping of 2 dimensional space
Published 2015“…The simulated robot is equipped with a sonar sensor and several infrared sensors on its chassis. …”
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6
Performance comparison of differential evolution and particle swarm optimization in constrained optimization
Published 2012“…Particle swarm optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms. …”
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7
Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network
Published 2020“…Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. …”
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8
Crossover-first differential evolution for improved global optimization in non-uniform search landscapes
Published 2015“…The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. …”
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9
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior
Published 2023“…This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of territorial predators in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). …”
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10
Provisioning an energy efficient with maximum coverage WSN through biological inspired sensor node placement
Published 2023Conference Paper -
11
Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm
Published 2020“…Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. …”
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12
A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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13
Multi-objective optimization of two-stage thermo-electric cooler using differential evolution: MO optimization of TEC using DE
Published 2015“…In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. …”
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14
Node placement optimization using extended virtual force and cuckoo search algorithm in wireless sensor network
Published 2014“…This study proposed Extended Virtual Force and Cuckoo Search (EVFCS) algorithm with a combination of EVFA and CS algorithm to find an optimal node placement. …”
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15
Multi-objective biological mimicry optimization algorithm for WSN sensor node placement
Published 2023Conference Paper -
16
Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks
Published 2023“…Keywords: anchor nodes; metaheuristic optimization algorithm; node localization; tent chaotic mapping; wireless sensor networks…”
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Wireless sensor network deployment performance based on FOA, PSO and TPSMA / Nurhidayah Kamal Akbar, Husna Zainol Abidin, and Ahmad Ihsan Mohd Yassin
Published 2019“…This paper compares the random deployment with other two algorithms known as Fruit Fly Optimization (FOA) and Particles Swarm Optimization (PSO) Territorial Predator Scent Marking Algorithm (TPSMA) to solve the coverage hole problem. …”
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18
The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment
Published 2016“…Thus, a new algorithm known as infection evolution (IE) was developed in this study. …”
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19
Optimized differential evolution algorithm for linear frequency modulation radar signal denoising
Published 2013“…The main contention of this thesis is to investigate the development of new optimization technique based on Differential Evolution algorithm (DE), applied for radar signal denoising application. …”
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20
An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks
Published 2015“…We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. …”
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