Introduces parallel subproblem evaluation and batch addition of up to K cuts per iteration for a convex vector optimization algorithm, proves the batch variant preserves the O(k^{2/(1-q)}) convergence rate, and reports 62-80% fewer iterations with variable wall-clock gains.
Multicriteria optimization using a genetic algorithm for determining a Pareto set,
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On Parallel and Batch-Cutting Strategies for Norm-Minimization-Based Convex Vector Optimization
Introduces parallel subproblem evaluation and batch addition of up to K cuts per iteration for a convex vector optimization algorithm, proves the batch variant preserves the O(k^{2/(1-q)}) convergence rate, and reports 62-80% fewer iterations with variable wall-clock gains.