Tag: Optimization problem
Probabilistic branch-and-bound can approximate Pareto-optimal sets
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in AlgorithmWhat the study found The study presents Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)), an algorithm for approximating the Pareto optimal set and the associated efficient frontier in stochastic multi-objective optimization problems. Why the authors say this matters The authors indicate that the algorithm is intended to handle noisy objective evaluations more…
Review finds Thomson encoding and Grover selection central in QGAs
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in AlgorithmWhat the study found The paper concludes that the encoding used for the Thomson problem, a problem about arranging particles on a sphere, is a decisive step toward using quantum genetic algorithms in a range of physical applications. It also concludes that Grover's search, a quantum search method, as a selection step in Reduced QGAs…
Hybrid scheduling methods improve bus driver assignment results
What the study found The study found that a tightly integrated mix of exact optimization and heuristic search produced state-of-the-art results for the bus driver scheduling problem, which is the task of designing driver shifts to cover planned bus tours under legal and contract rules. The authors report exact solutions for small instances and low…
Intelligent system supports multicriteria investment project optimization
Researchers developed an intelligent software system using network models and vector optimization to manage investment projects adaptively under multiple objectives and uncertainty.
AlphaLearn frames adaptive e-learning as multi-objective optimization
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AlphaLearn proposes a multi-objective evolutionary framework for adaptive e-learning pathways that integrates fairness as a core optimization criterion alongside learning effectiveness and engagement.
AGENT improved makespan in heterogeneous cloud task allocation
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AGENT framework improves task allocation in cloud systems using elitism-guided genetic algorithm with adaptive parameters, achieving 3-29% makespan improvements for heterogeneous VM scheduling.
