$ show intLyc/MTO-Platform
MTO-Platform (MToP) is a comprehensive MATLAB benchmarking platform for evolutionary multitasking (EMT). It provides a unified, extensible framework for defining optimization problems, implementing algorithms, and evaluating performance metrics. The platform includes over 50 multitask algorithms (e.g., MFEA, AT-MFEA, TRADE, SSLT), 200+ multitask problem cases (including CEC, WCCI, and real-world benchmarks), and 20+ performance metrics (IGD, HV, Spread, etc.). It supports single-task, multi-task, many-task, multi-objective, constrained, and competitive optimization scenarios. The system features both a graphical user interface (GUI) and a command-line interface, with parallel execution support and integration with external tools like IOHanalyzer and Python Gym environments for policy search. The architecture is modular: base classes (Problem, Algorithm, Metric) define consistent interfaces, and all components inherit from them. Utility modules (Individual, Initialization, Selection, Operator, Multi-objective sorting) are shared across algorithms, promoting code reuse. The codebase also includes real-world applications such as global trajectory optimization, optimal power flow, sensor coverage, and photovoltaic model parameter extraction. While primarily a research platform, it also serves as a library of reusable evolutionary computation components. The repository is actively maintained (v1.10, references to 2026) but exhibits some technical drift: use of deprecated MATLAB functions (textread), large vendored third-party code (SOM Toolbox), and binary .mat files that may cause compatibility issues. Code duplication exists in problem definitions and some algorithm utilities. Despite these issues, the platform is well-structured, extensively documented, and widely cited in the EMT literature.
The LLM identified 5 files worth highlighting for reuse:
Base class for all individual representations in the platform. Defines core properties (Dec, Obj, CV, Con) and methods for copying, comparing, and evaluating individuals. This class is the foundation for specialized individuals (Individual_DE, Individual_MF, Individual_PSO, etc.) and can be extracted as a standalone solution representation for any evolutionary algorithm framework.
Implements tournament selection with epsilon-level constraint handling. Supports both single-objective and multi-objective scenarios by comparing individuals based on constraint violation and objective values. The function is self-contained, parameterized by tournament size, and can be reused in any evolutionary algorithm requiring constrained selection.
Efficient non-dominated sorting algorithm (O(MN^2)) for multi-objective optimization. Returns front numbers and crowding distances. This implementation is adapted from PlatEMO but is self-contained, well-documented, and can be extracted for any multi-objective evolutionary algorithm framework.
Trains a Generative Adversarial Network (GAN) for data augmentation in evolutionary multitasking. Uses fully connected generator and discriminator with Adam optimization, batch normalization, and custom dropout. Demonstrates integration of deep learning with evolutionary optimization. The training loop pattern (shuffle, batch, gradient computation, parameter update) is reusable for other GAN-based transfer learning scenarios.
Trains a neural network with Monte Carlo dropout for uncertainty estimation. Implements a custom dropout layer and training procedure that returns both mean and variance predictions. This pattern is reusable for any application requiring uncertainty quantification in neural network predictions, such as surrogate-assisted optimization or active learning.