Adaptive Constraint Coefficient Tuning in Local Search Optimization
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Solution Overview
Problem
Conventional methods for adaptively adjusting the constraint coefficient in combinatorial optimization problems face inefficiencies as they fail to update the solution based on problem-specific constraints, leading to potential constraint violations and reduced search efficiency.
Innovation Solution
A data processing apparatus and method that dynamically adjusts the constraint coefficient by changing its value in the direction of approaching a target value, using a look-up-table to optimize the search for constraint satisfaction solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the constraint coefficient is increased to reduce constraint violations, then the reliability of constraint satisfaction improves, but the search efficiency deteriorates due to increased evaluation function values for constraint violation solutions
Solution Approach 1:
The patent applies dynamics by making the constraint coefficient variable rather than fixed. The coefficient is dynamically adjusted during the local search process based on the current solution's constraint violation status and the search progress, allowing the system to adapt the balance between constraint satisfaction and search efficiency at different stages of optimization.
Solution Approach 2:
The patent implements parameter changes by modifying the constraint coefficient value during the optimization process. The coefficient transitions from an initial value to a final value through predetermined changes, enabling the evaluation function to adapt its penalty strength dynamically. This allows the system to explore the solution space more effectively while maintaining constraint satisfaction.
2Productivity
If the constraint coefficient is decreased to improve search efficiency, then the productivity improves, but the reliability deteriorates as constraint violation solutions become more likely
Solution Approach 1:
The system dynamically adjusts the constraint coefficient based on the local search progress and solution quality. When the search is in early stages or constraint violations are detected, the coefficient increases to enforce constraints. When the search approaches convergence or constraint satisfaction is achieved, the coefficient decreases to allow finer exploration, thus balancing efficiency and reliability throughout the optimization process.
Solution Approach 2:
The patent incorporates feedback mechanisms where the constraint coefficient adjustment is based on monitoring the solution candidate's constraint satisfaction status and evaluation function values. This feedback loop enables the system to respond to the search process outcomes and adapt the coefficient accordingly, improving both constraint satisfaction and search efficiency.
3Device complexity
If a fixed constraint coefficient is used to simplify the method, then the device complexity is reduced, but the adaptability deteriorates as the coefficient cannot adjust to different problem conditions
Solution Approach 1:
The patent transforms the static fixed coefficient into a dynamic adaptive coefficient that automatically adjusts during the local search process. The coefficient changes are governed by predetermined rules that respond to the search progress and solution characteristics, providing adaptability without requiring complex external control mechanisms or manual tuning for different problems.
Data Source
AI summary
A storage unit stores evaluation function information of an evaluation function of a combinatorial optimization problem including a sum of an objective function and one or more constraint functions including a first constraint function weighted by a first coefficient (t) representing a weight of a first constraint condition. When repeatedly executing a local search for searching for a solution candidate (x*) of the combinatorial optimization problem using the evaluation function information while changing a value of t, a processing unit changes the value of t in a direction in which a value (V(x*)) of the first constraint function corresponding to x* obtained by the local search using each value of t approaches a target value (Vtarget) that is a positive value.