Ammunition Task Assignment Using Greedy Genetic Co-Evolution
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Solution Overview
Problem
Traditional genetic algorithms for multi-platform air defense task assignment are complex, slow in convergence, and inefficient due to reliance on initial parameter selection and lack of timely feedback, leading to increased computation time and difficulty in solving NP-complete problems with large numbers of interceptors and targets.
Innovation Solution
An intelligent ammunition co-evolution task assignment method using a greedy principle-based improved genetic algorithm, which encodes chromosomes for ammunition assignment, calculates fitness based on threat and interception probability, and performs crossover and mutation to quickly converge on optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional genetic algorithm is used for multi-platform air defense task assignment, then the assignment can be performed, but the convergence speed is slow and computation time is excessive
Solution Approach 1:
The patent applies preliminary action by using a greedy algorithm to perform preliminary task assignment before the genetic algorithm optimization. The greedy algorithm pre-assigns tasks based on simple evaluation criteria, providing a good initial population that is closer to the optimal solution, thereby reducing the number of generations needed for convergence and decreasing overall computation time.
2Quantity of substance
If the number of interceptors and targets increases, then the coverage is improved, but the difficulty of solving the assignment problem increases exponentially
Solution Approach 1:
The patent applies segmentation by dividing the large-scale assignment problem into smaller sub-problems. The genetic algorithm processes the assignment task in distributed or parallel manner, breaking down the complex NP-complete problem into manageable chunks that can be solved more efficiently, thus handling larger numbers of interceptors and targets without exponential complexity increase.
3Reliability
If centralized method is used for target assignment, then the optimization is comprehensive, but the computation difficulty increases exponentially with problem size
Solution Approach 1:
The patent introduces the greedy algorithm as an intermediary that bridges between simple assignment and complex genetic algorithm optimization. The greedy algorithm provides a preliminary assignment that serves as a starting point, reducing the search space for the genetic algorithm and making the comprehensive optimization more tractable even for large-scale problems.
4Ease of operation
If traditional genetic algorithm parameters are selected based on experience, then the algorithm can run, but the solution quality is affected and more training time is needed
Solution Approach 1:
The patent applies feedback by using the greedy algorithm's evaluation results to guide the genetic algorithm's parameter selection and optimization process. The feedback from the preliminary greedy assignment helps in setting appropriate initial parameters and adjusting them during evolution, improving solution quality while reducing the need for extensive trial-and-error parameter tuning.
Data Source
AI summary
An intelligent ammunition co-evolution task assignment method is disclosed. The method includes the following steps. A chromosome gene encoding of genetic algorithm for ammunition assignment scheme of multi-platform interception operation is performed. A fitness of chromosome individual function in a genetic population is calculated according to a threat degree of an intercepting target in the ammunition assignment scheme and an interception probability of the intercepting target by different ammunition launching platforms. A probability ranking sequence of effective interception of the intercepting target by different ammunition launching platforms is obtained, and a genetic algorithm selection operation is performed on the chromosome individuals according to probability values of effective interception in descending order of priority. Crossover and/or mutation are performed on the selected chromosome individuals to obtain a next generation genetic population and the above steps are repeated until termination conditions are met to obtain the final ammunition assignment scheme.