Adaptive Offset for Simulated Annealing Optimization
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Solution Overview
Problem
Existing optimization devices face challenges in efficiently escaping local solutions during simulated annealing due to inadequate offset value management, leading to prolonged calculation times and reduced accuracy.
Innovation Solution
An optimization device that calculates energy changes for state transitions and stochastically determines the acceptance of these transitions by adding an offset value based on a relative relation between the energy change and thermal excitation energy, using a coefficient to adaptively adjust the offset increment value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If an offset value is added to accelerate escape from local solutions, then calculation time is reduced, but solution accuracy deteriorates
Solution Approach 1:
The offset value is dynamically adjusted based on the current temperature and historical energy change statistics rather than being fixed. The adaptive offset value changes over time according to the system state, allowing aggressive escape early in the process when temperature is high, and more conservative behavior later when temperature is low and solution accuracy is critical.
Solution Approach 2:
The offset value parameter is modified based on statistical parameters (mean and standard deviation of energy changes) and temperature. By changing the offset value according to these parameters, the system adapts its escape behavior to match the current stage of the annealing process and the characteristics of the energy landscape being explored.
2Adaptability or versatility
If the offset value is increased to escape local solutions, then the branching ratio improves, but the energy calculation precision deteriorates
Solution Approach 1:
The offset value is determined as a function of temperature and statistical parameters (mean μ and standard deviation σ of energy changes). This parameter-based approach allows the offset to adapt to the current energy landscape characteristics, maintaining appropriate branching ratios without arbitrarily distorting energy calculations.
Solution Approach 2:
The system uses feedback from historical energy change data (mean and standard deviation) to adjust the offset value. This feedback mechanism ensures that the offset adapts to the actual energy landscape being explored, maintaining proper branching behavior while avoiding arbitrary additions that would distort energy precision.
3Measurement precision
If simulated annealing is performed with basic energy comparison, then solution accuracy is maintained, but calculation time increases due to prolonged trapping in local solutions
Solution Approach 1:
The offset value is pre-calculated based on statistical parameters (mean and standard deviation of energy changes) and temperature before actual state transitions are evaluated. This preliminary determination of the offset allows the system to quickly evaluate whether to accept transitions without iterative adjustment, reducing calculation time while maintaining accuracy through statistically-informed offset selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach accelerates the escape from local solutions, reduces calculation time, and maintains solution accuracy by dynamically adjusting the offset value to ensure appropriate branching ratios in simulated annealing.
Implementation Method 1
stochastically determine whether or not any of the plurality of state transitions is accepted, by a relative relation between the first change value and thermal excitation energy based on the temperature value
Implementation Method 2
The optimization device uses, for example, a probabilistic search method, such as simulated annealing, to find a combination of values of bits from which the minimum value among values (referred to as energy) of an energy function
Data Source
AI summary
A processor holds the second change value of the energy value which has been calculated by the processor and corresponds to each of a predetermined number of state transitions, in entries corresponding to the input identification information k. When the processor stochastically determines whether or not any of the state transitions is accepted, by a relative relation between the first change value and thermal excitation energy based on the temperature value, the first change value of the energy value calculated by the processor, and a random number value, the processor stochastically determines whether or not any of the state transitions is accepted, by adding the offset value y to the first change value. The offset value y is obtained by multiplying the second change value held by any entry selected from the entries based on the input identification information k, by coefficient information α.