Modified Genetic Algorithm Cross-Over Operator for Cloud Server Optimization
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Solution Overview
Problem
Standard genetic algorithms are limited in solving complex multi-element problems as they decompose and iterate through each element independently, failing to optimize solutions across all elements simultaneously and cannot handle constraints, leading to the exclusion of potentially optimal combinations due to constraints like no repeated elements in a solution.
Innovation Solution
A modified genetic algorithm is implemented with a processing logic that generates solutions based on criteria, uses a modified cross-over operator to combine solutions while maintaining constraints, and introduces mutation operations to generate more potential solutions, ensuring compliance with constraints like no repeated elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard genetic algorithms decompose and iterate through each element independently, then the algorithm is simple to implement, but it fails to optimize solutions across all elements simultaneously and cannot handle constraints
Solution Approach 1:
The patent modifies the standard genetic algorithm by changing the cross-over operator parameters and adding constraint-handling mechanisms. The modified cross-over operator uses index tracking and value sorting to ensure constraints are met during solution combination, transforming the algorithm from constraint-ignorant to constraint-aware while maintaining its iterative optimization framework
2Reliability
If standard genetic algorithms use traditional cross-over operations, then the operation is simple, but it excludes potentially optimal combinations due to constraints like no repeated elements
Solution Approach 1:
The modified cross-over operator performs preliminary actions by appending indices to solution elements before combination and sorting values based on those indices after cross-over. This preliminary indexing and subsequent sorting ensures constraint compliance is built into the cross-over process itself, preventing invalid combinations from being generated in the first place
Solution Approach 2:
The patent introduces indices as an intermediary mechanism between the cross-over operation and the final solution. These indices track element origins and enable the sorting step that ensures no repeated elements appear in combined solutions, acting as a mediator that reconciles the need for combination with the constraint against duplication
3Measurement precision
If more potential solutions are considered, then the optimized solution quality improves, but the computational complexity increases
Solution Approach 1:
The patent introduces dynamic mutation operations that adaptively generate additional potential solutions during the optimization process. Rather than statically considering all possible combinations, the dynamic mutation operator selectively explores promising regions of the solution space by introducing controlled variations, balancing thorough exploration with computational efficiency
Data Source
AI summary
A multi-element problem may be solved iteratively by using a modified genetic algorithm to generate a plurality of solutions according to a set of solution criteria. The solution criteria may comprise a plurality of servers, each server including one or more attributes, and an indication of which of the one or more attributes are to be optimized. An index may be appended, by a processing device, to each solution in the plurality of solutions and the values in each solution may be sorted. For each solution in the plurality of solutions, one or more values from the solution may be combined with one or more values from another solution to generate a plurality of child solutions. Each child solution may have an index, and the values in each child solution may be sorted in view of the child solution's index. For one or more child solutions in the plurality of child solutions, two selected values may be rearranged to generate one or more mutated child solutions.