What Multi-Objective Optimization got wrong, from 41 theses

Tried and failed

unclamped linear combination multi-objective fitness function applied to multi-target molecular property optimization. Outcome: worse than baseline. Reason: unbounded objective contributions distorted the search landscape, severely reducing multi-target success rates

Design of (De)Polymerizable Polymers Using Machine Learning-Based Predictive Models and Generative Algorithms · Georgia Tech · PhD

Tried and failed

directly optimizing ratio of objectives applied to multi-objective trade-off optimization. Outcome: worse than baseline. Reason: optimizing quality-to-cost ratio degraded the Pareto frontier and reduced outcome quality versus optimizing quality alone

Essays on developing artificial intelligence solutions for patient-centered healthcare delivery · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected scalarized single-objective fixed trade-off penalty as primary method because it requires arbitrary weighting and fails to properly explore the multi-objective Pareto trade-off frontier.

Fairness-Aware Unsupervised Learning Methods for Healthcare Applications · Penn · PhD

Tried and failed

Single-objective extreme weighting in multi-objective heuristic search applied to constrained spatial path finding. Outcome: worse than baseline. Reason: Extremes degrade search efficiency and Pareto trade-offs compared to balanced joint optimization.

Semantically diverse and spatially constrained queries · Iowa State · PhD

Tried and failed

extreme single-objective weighting in multi-objective optimization applied to coupled multidisciplinary systems design. Outcome: did not converge. Reason: extreme objective weights create severe gradient scaling imbalances and poor conditioning for gradient-based solvers

An Integrated Vehicle, Payload, and Trajectory Optimization Framework for Highly-Coupled Aircraft Systems · MIT · PhD

Tried and failed

weighted-sum multi-objective optimization for spatio-temporal trade-offs applied to remote sensing trajectory and payload design. Reason: Optimizer favored spatial averaging over multi-pass temporal averaging under the specified resolution objectives

An Integrated Vehicle, Payload, and Trajectory Optimization Framework for Highly-Coupled Aircraft Systems · MIT · PhD

Considered and rejected

Considered and rejected: Rejected a single weighted-sum multi-objective function in favor of generating a Pareto frontier via the constraint method due to varying stakeholder priorities and hidden objectives.

Expanding Geographic Access to Fertility Care in the United States: A Statistical and Multiobjective Optimization Approach · Johns Hopkins · PhD

Considered and rejected

Considered and rejected: Rejected auxiliary weighted sum functions for multi-objective optimization because they impose fixed Pareto front assumptions

Evolution of Multiobjective Neuromodulated Neurocontrollers for Multi-Robot Systems · Carleton University · PhD

Considered and rejected

Considered and rejected: Rejected using lexicographic selection or weighted-sum multi-objective optimization for COVID-19 scoring in favor of Pareto optimization with NSGA-II followed by post-hoc selection based on prognostic agreement.

Evolutionary Optimization of Decision Trees for Interpretable Reinforcement Learning · University of Trento · PhD

Considered and rejected

Considered and rejected: Rejected standard weighted sum multi-objective approach in favor of epsilon-constraint combined with TOPSIS to eliminate subjective weight-selection bias.

Enhancing Thermoelectric Generator Performance · University of Windsor · PhD

Considered and rejected

Considered and rejected: Rejected relying purely on standard heuristic algorithms (GA, PSO, SA) for multi-objective resonator tuning due to excessive function evaluations and curse of dimensionality.

Optimization of Locally Resonant Metafoundations for the Protection of Industrial Tanks and Small Modular Reactors Subjected to Low-Frequency Seismic Waves · University of Trento · PhD

Considered and rejected

Considered and rejected: Rejected multi-objective RLHF (Reinforcement Learning from Human Feedback) due to training instability, extensive hyperparameter tuning requirements, and computational inefficiency compared to MODPO.

Essays on Digital Content Strategies: Creation, Diffusion, and Monetization · Harvard · PhD

Considered and rejected

Considered and rejected: rejected multi-objective Bayesian optimization (MOBO) due to requirements of sampling across all preferences and inability to efficiently update distributions sequentially

Preserving Correct Behaviors during Neural Network-based Control Policy Repair · Penn · PhD

Considered and rejected

Considered and rejected: Rejected Multi-Objective Genetic Algorithm (MOGA) and ε-constraint optimization due to high computational overhead.

Optimum performance controls of multiport converter for transport electrification applications · Nottingham · PhD

Tried and failed

exact constructive solid geometry Boolean union operations applied to multi-objective geometric shape optimization. Outcome: too slow. Reason: computational overhead increased runtime nearly threefold without improving the Pareto hypervolume solution quality

Early Phase Performance Driven Design Assistance Using Generative Models · Harvard · Doctorate

Considered and rejected

Considered and rejected: A posteriori multi-objective preference articulation rejected for real-time MPC due to high computational effort of executing multiple optimization runs with different weights.

Energy management for industrial plants with thermal batch processes Energiemanagement für Industrieanlagen mit thermischen Batch-Prozessen · TU Wien · PhD

Considered and rejected

Considered and rejected: Rejected Pareto front multi-objective optimization approaches due to prohibitive computational costs in finding and representing the entire Pareto front.

Bridging the gaps in trustworthy autonomy: Alignment, validation, and physical execution in learning-enabled cyber-physical systems · Iowa State · PhD

Considered and rejected

Considered and rejected: Rejected multi-policy/separate network architectures for multi-objective RL due to high GPU/computational training burden in stable-baselines

Light Water Reactor Loading Pattern Optimization with Reinforcement Learning Algorithms · MIT · PhD

Tried and failed

simultaneous perturbation of all variables in simulated annealing applied to multi-objective system optimization. Outcome: did not converge. Reason: acting like ineffective random search, high-dimensional perturbations failed to find improved solutions

Multi-Objective System Optimization of a Mars Atmospheric ISRU Plant · MIT · PhD

Tried and failed

NSGA-II with default genetic operator parameters applied to multi-objective tri-level network optimization. Outcome: did not converge. Reason: fixed genetic crossover/mutation probability parameters hindered optimization convergence without adaptive tuning and offsets

Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility · Imperial · PhD

Tried and failed

Gaussian process Bayesian optimization with weighted expected improvement applied to multi-objective circuit parameter optimization. Outcome: did not converge. Reason: trapped in local optima and failed to satisfy multiple stringent constrained specifications simultaneously

Efficient optimization methods for analog/mixed-signal integrated circuits via machine learning · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected direct search and gradient-based optimization algorithms because direct search lacks precision for multi-objective trade-offs and gradient methods are prone to trapping in local extrema

DIGITAL CODESIGN OF A POWER MODULE WITH INTEGRATED THERMAL MANAGEMENT · Georgia Tech · PhD

Lost to a baseline

NSGA-II and MOPSO lost to HMOPSO on multi-objective benchmark functions (ZDT1, ZDT2, ZDT3, ZDT6), where NSGA-II showed low convergence speed and MOPSO suffered from particle aggregation/collapse.

Numerical simulation and optimization of fracturing-stimulated reservoir volume · Texas Tech · PhD

Tried and failed

L-BFGS-B gradient-based optimization applied to multi-objective quantum circuit parameter tuning. Outcome: did not converge. Reason: Trapped in local minima across the complex multi-objective optimization landscape.

Classical and quantum optimization of quantum processors · Harvard · PhD

Tried and failed

multi-objective Bayesian optimization with L-BFGS acquisition applied to high-dimensional benchmark functions. Outcome: did not converge. Reason: acquisition function optimization became under-parameterized when objectives approached input dimensionality

Multiscale Modeling and Microstructure Design of Biodegradable Polymeric Scaffolds · Georgia Tech · PhD

Lost to a baseline

ADD-MES and DPT-BO achieved smaller RX coil areas (19.26 mm2 and 11.04 mm2 vs 215.8 mm2) than the thesis's WPT multi-objective design

Scattering and Optimization Methods for Radar and Millimeter Wave Applications · Georgia Tech · PhD

Lost to a baseline

GA Method 4 test MAE on multi-objective optimization (0.067) was beaten by GA Method 2 (0.0655) and GA Method 1 (0.0658).

Data-Driven Discovery of Molecular Catalysts for Liquid Organic Hydrogen Carriers · University at Buffalo · PhD

Lost to a baseline

For memory overhead optimization in multi-objective tuning, Particle Swarm Optimization (PSO) underperformed Genetic Algorithm (GA) (inv_overhead score of 83% vs. 92%)

Resource-Efficient Edge Computing and Lightweight Traffic Fingerprinting for Scientific Applications · University of Nevada, Reno · Master's

Tried and failed

ensemble Pareto evolutionary algorithm applied to multi-objective combinatorial core optimization. Outcome: worse than baseline. Reason: Ensemble approach failed to outperform standalone evolutionary strategy across benchmark scenarios.

Light Water Reactor Loading Pattern Optimization with Reinforcement Learning Algorithms · MIT · PhD

Lost to a baseline

For time overhead optimization in multi-objective tuning, Artificial Bee Colony (ABC) achieved the lowest score compared to GA and PSO

Resource-Efficient Edge Computing and Lightweight Traffic Fingerprinting for Scientific Applications · University of Nevada, Reno · Master's

Lost to a baseline

MOMoT beat MDEOptimiser on multi-objective REF-EXT-A (median HV 0.53 vs 0.46)

Model-Driven Optimization with a Focus on the Effectiveness and Efficiency of Evolutionary Algorithms · University of Marburg · PhD

Tried and failed

multi-objective evolutionary optimization for single target priority applied to constrained resource allocation. Outcome: worse than baseline. Reason: Pareto compromise solutions underperform dedicated single-objective optimization when only one metric is prioritized

Integrating social equity into sustainable programming of road projects : a quantitative approach · UT Austin · PhD

Lost to a baseline

For individual single-objective scores (e.g., access metric f1 alone or precision f2 alone), single-objective adaptive allocations scored higher on their respective single metric than the multi-objective proposed method.

Operationalization of AI Systems Through Data-Centric Design · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected multi-objective simultaneous optimization across multiple stress-strain curves in favor of a sequential single-objective optimization isolated to specific dislocation mechanisms.

Modeling of overloads in cyclic loading · Cranfield · PhD

Considered and rejected

Considered and rejected: Rejected multi-objective Pareto optimization for trading off task time and energy consumption, choosing a single-objective weighted cost function where both metrics map directly to financial cost.

Reinforcement Learning and Trajectory Optimization for the Concurrent Design of high-performance robotic systems · University of Trento · PhD

Considered and rejected

Considered and rejected: Rejected multi-objective optimization yielding a Pareto front in favor of single weighted-sum fitness because users prefer converging on a single configuration solution.

Analyzing the impact of configurations in data-driven software applications · Iowa State · Master's

Tried and failed

multi-objective optimization generative tool in design process applied to architectural parametric structural design. Outcome: worse than baseline. Reason: algorithmic optimization tool led designers to produce lower quantitative performance than purely manual design exploration

Generative Design Tools: Implications on Design Process, Designer Behavior, and Design Outcomes · MIT · PhD

Considered and rejected

Considered and rejected: Rejected using only separate single-objective Bayesian optimizations for multi-objective problems because candidate solutions along the trade-off front were too sparsely distributed.

A DATA-DRIVEN APPROACH FOR MICROSTRUCTURAL INVERSE DESIGN THROUGH MATERIALS INFORMATICS · Johns Hopkins · PhD

Tried and failed

multi-objective genetic algorithm optimization applied to satellite constellation configuration design. Outcome: worse than baseline. Reason: Poor Pareto front diversity caused the algorithm to miss valid multi-satellite solutions and produce inferior coverage metrics.

Satellite Constellation Optimization for In-Situ Sampling and Reconstruction of Tides in the Thermospheric Gap · Virginia Tech · Master's

Considered and rejected

Considered and rejected: Rejected the Multi-Objective Evolutionary Algorithms NSGA-II, Borg, and SAMODE in favor of ε-NSGAII because they lacked approximate Pareto frontiers or used unsuitable dominance functions.

Ship and Naval Technology Trades-Offs for Science And Technology Investment Purposes · Georgia Tech · PhD

Lost to a baseline

Beam Search (BS) was outperformed by Multi-Objective Dynamic Programming (MODP) in retaining solution diversity across delta-v bands on a 1026-asteroid catalog.

Trajectory design of multi-target missions via graph transcription and dynamic programming. · Cranfield · PhD

Tried and failed

sequential multi-objective optimization applied to catalyst material candidate discovery. Outcome: worse than baseline. Reason: Optimizing properties sequentially prematurely restricts search space compared to simultaneous joint Hamiltonian optimization

Optimization Algorithms for Quantum and Digital Annealers · Harvard · PhD

Tried and failed

component substitution without joint parameter re-optimization applied to charged particle beam injector optimization. Outcome: worse than baseline. Reason: optimizing a single upstream parameter shifts the operating point away from the multi-objective Pareto front

Towards High Beam Brightness from Photocathodes · Cornell · PhD

Considered and rejected

Considered and rejected: Rejected merging calibration and retrofit multi-objective optimization into a single unified step due to distinct input parameter sets and conflicting objective functions

Rapid Workflow for Energy Model Calibration, Retrofit Optimization, and Uncertainty Analysis of Large Building Energy Retrofits · Carleton University · PhD

Left open

Develop multi-objective optimization algorithms to design macromolecules balancing functionality, synthetic accessibility, cost, and carbon footprint. Blocker: Lack of specific objectives, target datasets, property models, or optimization formulation

Designing Macromolecules using Machine Learning and Simulations · MIT · PhD

Left open

Incorporate multi-objective cost trade-offs into multi-fidelity batch Bayesian optimisation algorithms. Blocker: Lacks specific formulation, mathematical approach, or evaluation target for multi-cost trade-offs

Towards improving Bayesian optimisation for the physical sciences via long-term planning · Imperial · PhD

Left open

Formulate and evaluate multi-objective functions for hydrogen-CCS network optimization considering resource consumption and energy security alongside cost. Blocker: No specific multi-objective formulation, metrics, or mathematical definitions are provided.

Modelling framework for the design of hydrogen-CCS networks to decarbonise heating and industrial clusters · Imperial · PhD

Left open

Develop and integrate a multi-objective optimization decision-support tool for Pond-In-Pond wastewater treatment operational strategies. Blocker: Optimization criteria, specific situational constraints, and integration architecture with the hydrodynamic models are not defined.

Water sustainability using the Pond-In-Pond treatment system with reuse. · Texas Tech · PhD

Left open

Analyze multi-objective submodular optimization with noisy evaluation oracles and approximately submodular objective functions. Blocker: No concrete approach, target bounds, or formal evaluation criteria are specified

Submodular optimization problems in the context of social influence · Iowa State · PhD

Left open

Implement multi-objective Bayesian optimization models to simultaneously predict salt solubility, chemical compatibility, and conductivity in polymer electrolytes. Blocker: Lacks experimental or computational dataset for salt solubility and chemical compatibility corresponding to these polymer electrolyte formulations

Polymer Electrolyte Discovery via Rational Design and High Throughput Methods · MIT · PhD

Left open

Develop and implement a multi-objective Bayesian optimization acquisition function that relies on a single Gaussian process model instead of separate models. Blocker: None

Multiscale Modeling and Microstructure Design of Biodegradable Polymeric Scaffolds · Georgia Tech · PhD

Left open

Extend the surrogate optimizer to multi-objective Pareto optimization incorporating carbon valuations alongside financial metrics in agent-based retrofit simulation. Blocker: None

Investigating the use of Machine Learning for Resource Intensive Agent-Based Simulation and Optimisation of Domestic Energy Retrofit Adoption · Nottingham · PhD