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Reinitialize the entire swarm While this approach does neglect the swarm s memory and breaks the swarm out of a current optimum, it has the disadvantage that the swarm needs to search all over again If changes are not severe, the consequence is an unnecessary increase in computational complexity due to an increase in the number of iterations to converge to the new optimum The danger is also that the swarm may converge on a worse local optimum Reinitialization of the entire swarm is more e ective under severe environment changes Reinitialize only parts of the swarm, but make sure to retain the global best position The reinitialization of a percentage of the particle positions injects more diversity, and also preserves memory of the search space by keeping potentially good particles (existing particles may be close to the changed optimum) Hu and Eberhart experimented with a number of reinitialization percentages [386], and observed that lower reinitialization is preferred for smaller changes Larger changes require more particles to be reinitialized for e cient tracking It was also found empirically that total reinitialization is not e cient Particles memory of previous best positions is a major cause of the ine ciency of the PSO in tracking changing optima This statement is con rmed by the results of Carlisle and Dozier [107], where it was found that the cognition-only model showed poor performance The cognitive component promotes the nostalgic tendency to return to previously found best positions However, after a change in the environment, these positions become stale since they do not necessarily re ect the search space for the changed environment The consequence is that particles are attracted to outdated positions Carlisle and Dozier [107], and Hu and Eberhart [385] proposed that the personal best positions of all particles be reset to the current position of the particle when a change is detected This action e ectively clears the memory of all particles Resetting of the personal best positions is only e ective when the swarm has not yet converged to a solution [386] If this is the case, w(t)v(t) 0 and the contribution of the cognitive component will be zero for all particles, since (yi (t) xi (t)) = 0, i = 1, , ns Furthermore, the contribution of the social component will also be approximately zero, since, at convergence, all particles orbit around the same global best position (with an approximately zero swarm radius) Consequently, velocity updates are very small, as is the case for position updates To address the above problem, resetting of the personal best positions can be combined with partial reinitialization of the swarm [386] In this case, reinitialization increases diversity while resetting of the personal best positions prevents return to out-of-date positions Looking more carefully at the resetting of personal best positions, it may not always be a good strategy to simply reset all personal best positions Remember that by doing so, all particles forget any experience that they have gained about the search space during the search process It might just be that after an environment change, the personal best position of a particle is closer to the new goal than the current position Under less severe changes, it is possible that the personal best position remains the best position for that particle Carlisle and Dozier proposed that the personal best. microsoft azure ocr pdf Html to Pdf library in Azure Function - Stack Overflow
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