Virtual Exchange Platform for Black-Box Optimization
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Solution Overview
Problem
Current methods for solving black-box optimization problems are inefficient and require significant resources, as they often involve heuristic approaches that are not universally applicable and require substantial effort for parallelization and data learning, limiting their effectiveness across diverse and complex optimization scenarios.
Innovation Solution
A virtual exchange platform is introduced that allows users to submit black-box optimization problems, where candidate solutions can be collaboratively optimized by multiple users, with a simulator service executing these solutions to determine improvement values and reward contributors, thereby reducing computational and memory resources while providing a competitive and collaborative framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heuristic methods are used to solve generic black-box optimization problems, then solutions can be obtained for diverse problems, but significant computing resources, time, and effort are required for parameter fine-tuning, parallelization, and meta-model learning
Solution Approach 1:
The patent creates virtual copies of the optimization problem by containerizing the objective function and its environment. These virtual instances can be replicated and distributed across multiple computing nodes, allowing parallel evaluation of candidate solutions without requiring complex parallelization of the underlying optimization algorithms. Each containerized instance independently evaluates solutions, enabling efficient resource utilization across distributed systems.
Solution Approach 2:
The platform provides a universal interface that accepts diverse black-box optimization problems through standardized container formats. The system can handle various problem types (continuous, discrete, mixed, convex, non-convex) through a single unified platform that orchestrates multiple optimization algorithms and evaluation services, eliminating the need for problem-specific implementation details.
2Productivity
If substantial effort is invested in parallelization and learning from past data, then optimization performance can be improved, but implementation complexity and resource requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary layer consisting of container orchestration and platform management services that handle parallelization and resource allocation automatically. This intermediary abstracts the complexity of distributed computing from the optimization algorithms themselves, allowing researchers to focus on algorithm development while the platform manages the computational infrastructure, data distribution, and result aggregation.
3Measurement precision
If more computing resources are allocated to black-box optimization, then solution quality can be improved, but resource costs and time consumption increase
Solution Approach 1:
The platform enables partial evaluation of candidate solutions through early termination criteria and progressive refinement. Optimization algorithms can evaluate solutions to a certain precision threshold and stop when sufficient improvement is achieved, rather than exhaustively searching all possible solutions. This allows users to control the trade-off between solution quality and resource consumption based on their specific needs.
Data Source
AI summary
A data processing system for providing a black-box optimization exchange implements receiving a black-box optimization problem, presenting the problem on a user interface, receiving a candidate solution, inserting the candidate solution into a candidate solution queue, providing the candidate solution to a simulator service cause the simulator service to execute an objective function associated with the black-box optimization problem on the candidate solution, determining an improvement value provided by the candidate solution by analyzing one or more previously determined solutions to the first black-box optimization problem and the first objective value to determine an amount of improvement provided by the candidate solution, determining a reward by analyzing the improvement value using a reward function associated with the black-box optimization problem, and notifying the user that provided the candidate solution that the candidate solution has earned the user the reward.