# Multi-Objective Optimization

The open-source SRAM design tool OpenYieldV2 turns bitcell topology from a fixed input into an optimizable variable searched jointly with transistor sizing and threshold-voltage assignment. Previously OpenRAM generated layouts without optimizing, and OpenYield's high-fidelity optimization covered only a fixed 6T cell. OpenYieldV2 compiles 6T and 10T macros under identical high-fidelity conditions, predicts performance with a few-shot TabPFN surrogate, and searches with a coarse-to-refined multi-objective optimizer; in mixed-topology scenarios its hypervolume improves by up to 17.3% over the strongest single-topology baseline.

The open-source SRAM design tool OpenYieldV2 turns bitcell topology from a fixed input into an optimizable variable searched jointly with transistor sizing and threshold-voltage assignment. Previously OpenRAM generated layouts without optimizing, and OpenYield's high-fidelity optimization covered only a fixed 6T cell. OpenYieldV2 compiles 6T and 10T macros under identical high-fidelity conditions, predicts performance with a few-shot TabPFN surrogate, and searches with a coarse-to-refined multi-objective optimizer; in mixed-topology scenarios its hypervolume improves by up to 17.3% over the strongest single-topology baseline.

Internal distillation aligns a teacher model's intermediate activations with a student's smaller representation space, and existing methods do this by training extra projection matrices that are discarded after training. RAPID replaces them with a fixed random projection built from the Johnson-Lindenstrauss lemma and balances its losses with exponential moving average and homoscedastic uncertainty weighting. In reasoning post-training with Gemma 3 and Qwen 2.5, its four-benchmark averages stay within 0.15 percentage points of learned projection while trainable parameters fall 8.1% in the Qwen experiment and 21.1% when distilling all activations.

Internal distillation aligns a teacher model's intermediate activations with a student's smaller representation space, and existing methods do this by training extra projection matrices that are discarded after training. RAPID replaces them with a fixed random projection built from the Johnson-Lindenstrauss lemma and balances its losses with exponential moving average and homoscedastic uncertainty weighting. In reasoning post-training with Gemma 3 and Qwen 2.5, its four-benchmark averages stay within 0.15 percentage points of learned projection while trainable parameters fall 8.1% in the Qwen experiment and 21.1% when distilling all activations.

Multi-objective optimization struggles to balance convergence and spread on irregular or disconnected Pareto fronts. IMOCCO adds manifold-feature collision, mass-restitution collision and angular-sparsity guidance to the centered-collision search, acting on the search operator, individual interaction and environmental selection. Across 31 standard test functions it holds a Friedman mean rank of 1.73, first among seven algorithms, and scores 2/0/0 on the Wilcoxon test for a four-bar truss and a disc brake.

Multi-objective optimization struggles to balance convergence and spread on irregular or disconnected Pareto fronts. IMOCCO adds manifold-feature collision, mass-restitution collision and angular-sparsity guidance to the centered-collision search, acting on the search operator, individual interaction and environmental selection. Across 31 standard test functions it holds a Friedman mean rank of 1.73, first among seven algorithms, and scores 2/0/0 on the Wilcoxon test for a four-bar truss and a disc brake.

Optimizing biomass torrefaction experimentally is limited by cost and time, since one-factor-at-a-time trials miss synergistic effects and full factorial designs quickly become prohibitive. Using 486 torrefaction observations, Gaussian process regression with automatic relevance determination kernels reached cross-validated R² of 0.854 for mass yield, 0.971 for higher heating value and 0.869 for energy yield, outperforming random forest, support vector regression and a neural network. An acquisition function weighting uncertainty, mass-yield improvement and HHV improvement at 0.4/0.3/0.3, plus a diversity penalty, then recommended 10 experiments spanning 200–315 °C and 30–1089 s, all inside the conventional torrefaction window.

Optimizing biomass torrefaction experimentally is limited by cost and time, since one-factor-at-a-time trials miss synergistic effects and full factorial designs quickly become prohibitive. Using 486 torrefaction observations, Gaussian process regression with automatic relevance determination kernels reached cross-validated R² of 0.854 for mass yield, 0.971 for higher heating value and 0.869 for energy yield, outperforming random forest, support vector regression and a neural network. An acquisition function weighting uncertainty, mass-yield improvement and HHV improvement at 0.4/0.3/0.3, plus a diversity penalty, then recommended 10 experiments spanning 200–315 °C and 30–1089 s, all inside the conventional torrefaction window.

Federated aggregation no longer rewrites the global optimization direction to gain fairness. Earlier methods reweight clients, correct conflicting gradients, or treat client losses as symmetric multi-objective common descent, all of which change the global update direction. FedPCD keeps the conventional FedAvg aggregate as the primary objective and individual client updates as secondary constraints, applying only the minimum correction needed when a constraint is violated; across CIFAR-10, CIFAR-100 and Fashion-MNIST it reaches the highest global accuracy in all 18 configurations and the highest worst-10% client accuracy in 17 of them.

Federated aggregation no longer rewrites the global optimization direction to gain fairness. Earlier methods reweight clients, correct conflicting gradients, or treat client losses as symmetric multi-objective common descent, all of which change the global update direction. FedPCD keeps the conventional FedAvg aggregate as the primary objective and individual client updates as secondary constraints, applying only the minimum correction needed when a constraint is violated; across CIFAR-10, CIFAR-100 and Fashion-MNIST it reaches the highest global accuracy in all 18 configurations and the highest worst-10% client accuracy in 17 of them.

The work introduces the first approach for multi-objective optimization of resource-level handover policies in business processes: taking a Multi-Agent System (MAS) simulation model as input, it uses NSGA-II to search for Pareto-optimal person-specific handover policies, and across five synthetic Loan Application variants and seven real event logs it reduces cost by an average of 37% and waiting time by 58% relative to the as-is process, outperforming availability, random, lowest-cost (LC), and shortest-processing-time (SPT) heuristic baselines in most settings.

The work introduces the first approach for multi-objective optimization of resource-level handover policies in business processes: taking a Multi-Agent System (MAS) simulation model as input, it uses NSGA-II to search for Pareto-optimal person-specific handover policies, and across five synthetic Loan Application variants and seven real event logs it reduces cost by an average of 37% and waiting time by 58% relative to the as-is process, outperforming availability, random, lowest-cost (LC), and shortest-processing-time (SPT) heuristic baselines in most settings.