Stochastic Simulation Parameter Tuning for Global Cost Optimization
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Solution Overview
Problem
Existing stochastic simulation algorithms for cost function optimization often fail to efficiently converge to global maxima or minima due to poorly set control parameters, which are typically determined by testing on a small sample of test problems and may not account for differences in cost function behavior, leading to inefficient performance when applied to significantly different problems.
Innovation Solution
A computing device is configured to dynamically tune control parameter values by computing a control parameter upper bound, lower bound, and intermediate values within a defined range, adjusting these parameters based on transition probabilities and diffusion speeds to optimize the stochastic simulation algorithm's performance for specific cost functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If control parameters are determined by testing on a small sample of test problems, then the setup process is simple and quick, but the algorithm performance deteriorates when applied to significantly different cost functions
Solution Approach 1:
The patent dynamically adjusts control parameters (such as temperature in simulated annealing) based on the specific characteristics of the cost function being optimized. Instead of using fixed parameters determined from test samples, the system computes parameters adaptively during the optimization process, changing them according to the actual behavior of the cost function to improve convergence efficiency.
Solution Approach 2:
The optimization algorithm performs self-tuning by automatically determining its own control parameters based on observed performance and cost function characteristics. The system monitors its own progress and adjusts parameters autonomously without requiring external configuration or test-based pre-setting, enabling it to adapt to different cost functions independently.
2Device complexity
If traditional stochastic simulation algorithms are used with fixed control parameters, then the algorithm is simple to implement, but it fails to efficiently converge to global maxima or minima for different cost functions
Solution Approach 1:
The patent transforms the static control parameters into dynamic ones that evolve during the optimization process. The control parameters are no longer fixed values but change over time based on the optimization progress and cost function characteristics, allowing the algorithm to adapt its behavior to different problems while maintaining a relatively simple implementation framework.
Data Source
AI summary
A computing device is provided, including memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound. The processor may compute a control parameter lower bound. The processor may compute a plurality of intermediate control parameter values within a control parameter range between the control parameter lower bound and the control parameter upper bound. The processor may compute an estimated minimum or an estimated maximum of the cost function using the stochastic simulation algorithm with the control parameter upper bound, the control parameter lower bound, and the plurality of intermediate control parameter values. A plurality of copies of the cost function may be simulated with a respective plurality of seed values.