Hybrid Genetic Algorithm for Weapon Threat Allocation
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Solution Overview
Problem
Existing target-weapon pairing systems fail to model weapon resource and temporal constraints, leading to inefficient allocation and scheduling of defensive weapons, resulting in potential delays and unengaged threats, especially in scenarios with multiple overlapping threats and limited resources.
Innovation Solution
A hybrid genetic algorithm combining traditional genetic algorithms with simulated annealing, which models engagement resource and temporal constraints to optimize the allocation and scheduling of defensive weapons, determining both which threats to assign to a weapon system and when to deploy them, ensuring maximal resource utilization and effective battle management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional genetic algorithms are used for weapon allocation, then the system can handle large problem sizes, but the solution speed is relatively slow and may give sub-optimal solutions
Solution Approach 1:
The patent combines traditional genetic algorithms with simulated annealing to create a hybrid algorithm. The genetic algorithm provides global search capability for handling large problem sizes, while simulated annealing enhances local optimization and escape from local minima. This merging allows the system to achieve both fast computation and high-quality near-optimal solutions for weapon allocation problems.
2Ease of operation
If static-weapon target allocation is used, then the system is simple to implement, but it cannot optimize the deployment or launch time of the weapon system
Solution Approach 1:
The patent transitions from static weapon-target allocation to dynamic allocation by incorporating temporal dimensions. The system determines not only which weapons should engage targets but also optimal deployment and launch times. This dynamic approach allows the system to adapt to changing battle conditions and optimize engagement timing, significantly improving effectiveness while maintaining computational tractability through the hybrid algorithm.
3Reliability
If centralized decision-making processes are used to allocate threats to weapon systems, then the system can coordinate engagement, but the complexity increases with large numbers of threats and weapon systems
Solution Approach 1:
The patent replaces complex manual centralized decision-making with an automated hybrid genetic algorithm. The algorithm systematically evaluates numerous threats and weapon systems, considering factors such as threat severity, weapon effectiveness, and temporal constraints. This substitution of mechanical manual decision-making with computational algorithms reduces human cognitive load and complexity while maintaining or improving coordination effectiveness through optimized automated allocations.
4Productivity
If weapon systems are allocated threats without modeling resource and temporal constraints, then the allocation process is fast, but the weapon system may not have sufficient time-critical resources to engage threats
Solution Approach 1:
The hybrid algorithm performs preliminary evaluation of resource and temporal constraints before finalizing weapon-target allocations. It models engagement requirements, guidance time, and weapon system capabilities in advance to ensure that allocated threats can actually be engaged with available resources. This preliminary constraint modeling prevents infeasible allocations while maintaining computational efficiency through the optimized search strategy.
Data Source
AI summary
Method and apparatus to automatically allocate and schedule weapon systems to threats for maximizing an engagement objective. In one aspect, methods and systems maximize threat killed. In another aspect, methods and systems maximize asset survival against threats. The methods and apparatus considers temporal and resource constraints such that weapons systems are able to engage threats assigned to them.