Search Results - (( evolution optimization path algorithm ) OR ( waste reduction model algorithm ))
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The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment
Published 2016“…Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). …”
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Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
Published 2024“…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim
Published 2007“…Roadways and telephone systems are the examples of them. Genetic Algorithms (GA), pioneered by John Holland, applies the principle of evolution found in nature to the problem of finding an optimal solution. …”
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Differential evolution optimization for constrained routing in Wireless Mesh Networks
Published 2014“…This solution addresses efficient and optimal routing path construction for cost and quality metrics of the application. …”
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Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization
Published 2023“…Bins; Budget control; Carbon dioxide; Cost reduction; Costs; Crashworthiness; Efficiency; Environmental impact; Fuels; Learning algorithms; Optimization; Solid wastes; Vehicle routing; Backtracking search algorithms; Capacitated vehicle routing problem; Environmental emissions; Optimization modeling; Route optimization; Solid waste collection; Threshold waste level; Waste collection; Waste management; fuel; carbon dioxide; solid waste; algorithm; fuel consumption; modeling; optimization; solid waste; threshold; waste management; algorithm; Article; backtracking search algorithm; capacitated vehicle routing problem model; carbon footprint; controlled study; environmental impact assessment; feasibility study; limit of quantitation; measurement precision; process model; process optimization; solid waste collection; solid waste management; motor vehicle; procedures; solid waste; theoretical model; waste disposal; Algorithms; Carbon Dioxide; Models, Theoretical; Motor Vehicles; Refuse Disposal; Solid Waste…”
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Literature Review of Optimization Techniques for Chatter Suppression In Machining
Published 2011“…Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.…”
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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators
Published 2024“…By integrating the ABC algorithm into the manipulator's control system, the goal is to enhance its ability to plan paths and optimize trajectories. …”
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Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin
Published 2013“…This mechanism is restricted to search the possible solutions in a critical path. Modification on the path by using neighborhood search significantly reduces the total length of the makespan. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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EMAPlus-optimized adaptive convergence prescribed performance control for high-precision steering of rack steering vehicles
Published 2026“…EMAPlus is employed to jointly tune the ACPPC and AW-PI parameters, enabling fast, stable, and computationally efficient optimization compared with the original Evolutionary Mating Algorithm (EMA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). …”
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A greedy heuristics multiple criteria model for solving multi-landfill site selection and plant propagation algorithm for improving waste collection vehicle routing solutions
Published 2023“…This research focuses on two solid waste management (SWM) problems, particularly the landfill site selection problem (LSSP) and the waste collection vehicle routing problem (WCVRP). …”
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A secure trust aware ACO-Based WSN routing protocol for IoT
Published 2022“…The performance of the proposed routing algorithm is demonstrated through MATLAB. Based on the proposed system, to find the secure and optimal path while aiming at providing trust in IoT environment, the average energy consumption is minimized by nearly 50% even as the number of nodes has increased, as compared with the conventional ACO algorithm, a current ant-based routing algorithm for IoT-communication, and a present routing protocol RPL for IoT.…”
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Development of novel algorithm features in the modelling of cyclic processes
Published 2002“…The second feature of the algorithm is a reduction of the number of cycles required to reach the CSS while ensuring that the CSS is certainly determined. …”
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Dealing with Routing Hole Problem in Multi-hop Hierarchical Routing Protocol in Wireless Sensor Network
Published 2019“…The challenging issue of routing protocols is to reduce the communication overhead for data transmission by determining an optimal path. The hierarchical routing technique is one of the energy efficient routing protocols in WSN. …”
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A novel high performance routing technique for cross-referencing DMFBs
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management
Published 2024“…Evaluation metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and R-squared are commonly employed in the assessment of Machine Learning models' performance. The Voting regression, which leverages the collective predictive power of multiple models, exhibits superior performance in comparison to individual algorithms. …”
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