Search Results - (( based application _ algorithm ) OR ( its applications ((among algorithm) OR (ant algorithm)) ))*
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A novel swarm-based optimisation algorithm inspired by artificial neural glial network for autonomous robots
Published 2019“…Some of the examples highlighted by [13] are a combinatorial optimisation, routing communications network, as well as solving robotics applications. According to [13], the two best-known swarm intelligence algorithms are Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO). …”
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Ant system with heuristics for capacitated vehicle routing problem
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Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri
Published 2020“…Metaheuristic algorithms have been adapted in various engineering applications due to their efficiencies in solving complex and larger real-life optimization problems. …”
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Rule pruning techniques in the ant-miner classification algorithm and its variants: A review
Published 2018“…Rule-based classification is considered an important task of data classification.The ant-mining rule-based classification algorithm, inspired from the ant colony optimization algorithm, shows a comparable performance and outperforms in some application domains to the existing methods in the literature.One problem that often arises in any rule-based classification is the overfitting problem. …”
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Ant colony optimization algorithm for load balancing in grid computing
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Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid
Published 2012“…Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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A Hybrid Metaheuristic Technique Based on Grey Wolf Optimisation, Symbiotic Organism Search, and Ant Colony Optimisation for Solving Multi-Objective Vehicle Routing Problems
Published 2025“…For objective two, three routing strategies were evaluated. Based on a single-vehicle optimal route, the algorithm provided the shortest distance, 91.74 km. …”
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Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…With this aim, a number of forecasting methods have been proposed in different studies till now, among many soft computing-based approaches are the most successful ones as they offer high accuracy as well as application simplicity. …”
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Hybrid ant colony optimization algorithm for container loading problem
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Reactive memory model for ant colony optimization and its application to TSP
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Aco-based feature selection algorithm for classification
Published 2022“…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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Modified ACS centroid memory for data clustering
Published 2019“…Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. …”
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