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Early results of the application of the PSO to type I environments (refer to Section A9) with small spatial severity showed that the PSO has an implicit ability to track changing optima [107, 228, 385] Each particle progressively converges on a point on the line that connects its personal best position with the global best position [863, 870] The trajectory of a particle can be described by a sinusoidal wave with diminishing amplitude around the global best position [651, 652] If there is a small change in the location of an optimum, it is likely that one of these oscillating particles will discover the new, nearby optimum, and will pull the other particles to swarm around the new optimum However, if the spatial severity is large, causing the optimum to be displaced outside the radius of the contracting swarm, the PSO will fail to locate the new optimum due to loss of diversity In such cases mechanisms need to be employed to increase the swarm diversity Consider spatial changes where the value of the optimum remains the same after the change, ie f (x (t)) = f (x (t + 1)), with x (t) = x (t + 1) Since the tness remains the same, the global best position does not change, and remains at the old optimum Similarly, if f (x (t)) > f (x (t+1)), assuming minimization, the global best position will also not change Consequently, the PSO will fail to track such a changing minimum This problem can be solved by re-evaluating the tness of particles at time t + 1 and updating global best and personal best positions However, keep in mind that the same problem as discussed above may still occur if the optimum is displaced outside the radius of the swarm One of the goals of optimization algorithms for dynamic environments is to locate the optimum and then to track it The self-adaptation ability of the PSO to track optima (as discussed above) assumes that the PSO did not converge to an equilibrium state in its rst goal to locate the optimum When the swarm reaches an equilibrium (ie converged to a solution), vi = 0 The particles have no momentum and the contributions from the cognitive and social components are zero The particles will remain in this stable state even if the optimum does change In the case of dynamic environments, it is possible that the swarm reaches an equilibrium if the temporal severity is low (in other words, the time between consecutive changes is large) It is therefore important to read the literature on PSO for dynamic environments with this aspect always kept in mind The next aspects to consider are the in uence of particle memory, velocity clamping and the inertia weight The question to answer is: to what extent do these parameters and characteristics of the PSO limit or promote tracking of changing optima These aspects are addressed next: Each particle has a memory of its best position found thus far, and velocity is adjusted to also move towards this position Similarly, each particle retains information about the global best (or local best) position When the nvironment changes this information becomes stale If, after an environment change, particles are still allowed to make use of this, now stale, information, they are.

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Ij denotes the closed interval, Ij (t) = [xmin,j (t), xmax,j (t)] = {x : xmin,j x xmax,j }, Lj (t) is the score for the lower bound, and Uj (t) is the score for the upper bound In addition to the above knowledge components, the following knowledge components can be added [672, 752]: A domain knowledge component, which is similar to the situational knowledge component in that it stores the best positions found The domain knowledge component di ers from the situational knowledge component in that knowledge is not re-initialized at each generation, but contains an archive of best solutions since evolution started very similar to the hall-of-fame used in coevolution A history knowledge component, used in problems where search landscapes may change This component maintains information about sequences of environmental changes For each environmental change, the following information is stored: the current best solution, the irectional change for each dimension and the current change distance A topographical knowledge component, which maintains a multi-dimensional grid representation of the search space Information is kept about each cell of the grid, eg frequency of individuals that occupy the cell Such frequency information can be used to improve exploration by forcing mutation direction towards unexplored areas The type of knowledge components and the way that knowledge is represented have an in uence on the acceptance and in uence functions, as discussed in the following sections. In Visual Basic .NET Using Barcode encoder for . Draw GS1 128 In .NET Framework Using Barcode .Related: Excel Code 39 Generation , .NET UPC-E Generation , .NET WinForms UPC-E Generation

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