Optimization Apparatus Escaping Local Minimums via Adaptive Offset
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Simulated annealing methods for discrete optimization problems face challenges in efficiently escaping local minimums, leading to prolonged calculation times due to slow temperature reduction and difficulty in making appropriate adjustments, which results in a low probability of reaching optimal solutions.
Innovation Solution
An optimization apparatus with a transition control unit that adds an offset to energy changes and prohibits state transitions back to previous states, using an offset control circuit to increase the offset at local minimums and reset it when a state transition occurs, thereby promoting escape from local minimums and reducing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulated annealing is used to solve discrete optimization problems, then a solution can be found faster than precise algorithms, but the calculation time is prolonged due to slow escape from local minimums
Solution Approach 1:
The offset control circuit performs preliminary action by proactively increasing the offset value before the system gets trapped in a local minimum. By detecting when no state transitions occur (indicating a local minimum), the circuit pre-increases the offset to facilitate future escape, rather than waiting passively for thermal excitation to occur naturally.
Solution Approach 2:
The invention dynamically changes the offset parameter based on the system's state. When the system is at a local minimum (no transitions occur), the offset is increased to modify the energy landscape and enable escape. This parameter adaptation allows the system to balance exploration and exploitation, reducing calculation time while maintaining solution quality.
2Reliability
If temperature is reduced slowly to ensure proper annealing, then convergence is improved, but the duration spent at local minimums increases
Solution Approach 1:
The offset control circuit introduces dynamics into the annealing process by making the offset value adaptive rather than static. The offset changes dynamically based on whether state transitions are occurring, allowing the system to accelerate through local minimums while maintaining proper convergence characteristics during normal operation.
Solution Approach 2:
The system uses feedback from the state transition process to control the offset. When no transitions occur (feedback indicating a local minimum), the offset is increased. This feedback mechanism allows the system to respond to its own behavior and adjust parameters in real-time to optimize both convergence and speed.
3Loss of time
If the offset is increased to escape local minimums, then calculation time is reduced, but the system may overshoot optimal solutions
Solution Approach 1:
The offset increase is applied locally and temporarily only when needed (at local minimums), rather than globally throughout the entire annealing process. This localized application of the offset ensures that accuracy is maintained during normal operation while enabling escape from local minimums when necessary.
Solution Approach 2:
The system serves itself by automatically detecting when it is trapped in a local minimum and self-correcting by increasing the offset. This self-service mechanism eliminates the need for external intervention or complex control logic, allowing the system to maintain accuracy while improving its own performance.
Data Source
AI summary
When stochastically deciding, based on a change in energy and a random number relating to thermal excitation, whether to accept any of a plurality of state transitions according to the relative relationship between the change in energy and thermal excitation energy, a transition control unit adds an offset to the change in energy, performs control so that the offset at a local minimum where energy is locally minimized is larger than an offset when the energy is not minimized, holds transition information (a transition number) indicating a previous state transition, and prohibits, based on a decoding result for the held transition information, a first state transition out of the present state transition candidates.