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The dynamic buffer, of type CBufSeg, is instantiated at the beginning of the TestBuffersL() function by a call to CBufSeg::NewL() A 32-byte block of random data is retrieved from GetRandomDataLC() and inserted into the buffer at position 0, using nsertL() The example also illustrates how to pre-expand the buffer using ExpandL() to allocate extra space in the buffer at the position speci ed (alternatively, you can use ResizeL(), which adds extra memory at the end) These methods are useful if you know there will be a number of insertions into the buffer and you wish to make them atomic ExpandL() and ResizeL() perform a single allocation, which may fail, but if the buffer is expanded successfully then data can be added to the array using Write(), which cannot fail This is useful to improve performance, since making a single call which may leave (and thus may need to be called in a TRAP) is far more ef cient than making a number of InsertL() calls, each of which needs a TRAP2 Following the buffer expansion, random data is retrieved and written to the buffer in a series of short blocks The PrintBufferL() function illustrates the use of the Compress(), Size() and Read() methods on the dynamic buffers The Compress() method compresses the buffer to occupy minimal space, freeing any unused memory at the end of a segment for a CBufSeg, or the end of the at contiguous buffer for CBufFlat It s a useful method for freeing up space when memory is low or if the buffer has reached its nal size and cannot be expanded again The example code uses it in PrintBufferL() before calling Size() on the buffer (and using the returned value to allocate a buffer of the appropriate size into which data from Read() is stored) The Size() method returns the number of heap bytes allocated to the buffer, which may be greater than the actual size of the data contained therein, because the contents of the buffer may not ll the total allocated size The call to Compress() before Size() thus retrieves the size of the data contained in the buffer rather than the entire memory size allocated to it To retrieve data from the buffer, you can use the Ptr() function, which returns a TPtr8 for the given buffer position up to the end of the memory allocated For a CBufFlat this returns a TPtr8 to the rest of the buffer, but for CBufSeg it returns only the data from the given position to the end of that segment To retrieve all the data in a segmented buffer using Ptr(), you must use a loop which iterates over every allocated segment Alternatively, as I ve done in PrintBufferL(), the Read() method transfers data from the buffer into a descriptor, up to the length of the descriptor or the maximum length of the buffer, whichever is smaller.

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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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