Research Article
An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing
Input: The joint sparsity ; the wolf number set ; the limiting parameter ; the stopping criterion . | Initialization: Initialize the wolf’s position by using the Eq. (16); Initialize the best three positions by | , , ; | the iteration number ; the allowed maximum iterations number . | Judgement: if , set , output the joint signal by using the Eq. (13) and stop. Otherwise, go to the iteration. | Iteration: | Step 1. Update all wolves’ positions : | Step 1.1. Define . | Step 1.2. If , randomly choose elements from to forma set and define | . If , randomly choose elements from to form a set | and define . | Step 1.3. Use the least square method to estimate a temporary solution by using the Eq. (17). | Step 1.4. Update the wolf’s position by using the Eq. (18). | Step 2. Update the best three wolves’ positions: , , | . | Step 3. Check the terminate criterion: If or , set the final joint support set and terminate | the iteration. Otherwise, set and go to the next iteration. | Output: Estimate the joint signal by using the Eq. (13). |
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