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Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…A CAD model of a dashboard part is incorporated into a finite element analysis to measure shear and residual stresses. …”
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Multi-Objective Task Allocation Via Multi-Agent Coalition Formation
Published 2012Subjects: “…multi-objective programming…”
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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study
Published 2022Subjects: “…Multi-objective genetic algorithm Model calibration Indoor temperature Global sensitivity analysis Whole-building simulation…”
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Multi-Objective Co-optimization of Power and Gas under Uncertainties with P2H embedded
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Gas Metal Arc Welding (GMAW) Process Optimization Using Machine Learning Models
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A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and c cliques. …”
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A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS
Published 2020“…In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. …”
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Combining and adapting software quality predictive models by genetic algorithms
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A new bi-objective model of the urban public transportation hub network design under uncertainty
Published 2019“…For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. …”
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Quality estimation models optimization using genetic algorithms: Case of maintainability
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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An ant colony optimization algorithm to improve software quality prediction models
Published 2011“…Objective The objective of this paper is to present an approach that optimizes the accuracy of software quality predictive models when used to classify new data. …”
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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
Published 2023“…Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. …”
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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm
Published 2023“…This paper proposes a multi-agent technique for modeling and simulating multiple objects and multiple sensor tracking for drones. …”
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Squirrel Search Algorithm for Portfolio Optimization
Published 2019“…The inclusion of real-life constraints to the problem has led to the introduction of the extended Mean-Variance model. However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. …”
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