Electromagnetic Spectrum Allocation Method for Multi-Service Irregular Frequency-Using Devices in the Space–Air–Ground Integrated Network

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. 2022 Nov 27;22(23):9227. doi: 10.3390/s22239227

Algorithm 1 HNOGA pseudocode

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Input: population size M, number of elite individuals q, subpopulation neighborhood search probability $P_{m 1}$ , optimal individual neighborhood search probability $P_{m 2}$ , and maximum iteration number T;
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Output: the optimal individual $I_{b}$ ;
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Start:
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Step 1: The initial population with population size M was generated by random coding, the fitness of each individual in the initial population was calculated, and the top q individuals with the highest fitness were copied and saved $(q < M)$ . Studies show that when $q = 0.3 M$ , the performance of the algorithm is optimal.
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Step 2: Genetic algorithm optimization
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for iteration $t = 1 : T$ do
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Main genetic algorithm operation: random league selection, orthogonal uniform crossover, uniform mutation;
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All the individuals whose fitness is not 0 in the population and whose subpopulation contains greater than or equal to 2 individuals are optimized by the micro genetic algorithm with probability $P_{m 1}$ , and then the niche operation is performed;
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The individual with the highest fitness in the whole population was selected to perform micro genetic algorithm optimization with probability $P_{m 2}$ ;
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Perform niche operations;
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The top q individuals with the highest fitness in the population are copied and retained, and the top M individuals with the highest fitness are used to form the population of the next iteration.
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Step 3: Output the optimal individual $I_{b}$ of the last generation population;
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End: Obtain the optimal individual $I_{b}$ .