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an increase in the number of particles, but at the expense of increased computational complexity The basic PSO also fails another important objective of niching algorithms, namely to maintain niches If the search process is allowed to continue, more particles converge to the global best position in the process to reach an equilibrium, mainly due to the social component (as explained above Emanating from the discussion in this section, it is desirable to rather adapt the basic PSO with true speciation abilities One such algorithm, the NichePSO is described next The NichePSO was developed to nd multiple solutions to general multi-modal problems [89, 88, 91] The basic operating principle of NichePSO is the self-organization of particles into independent sub-swarms Each sub-swarm locates and maintains a niche Information exchange is only within the boundaries of a sub-swarm No information is exchanged between sub-swarms This independency among sub-swarms allows sub-swarms to maintain niches To emphasize: each sub-swarm functions as a stable, individual swarm, evolving on its own, independent of individuals in other swarms The NichePSO starts with one swarm, referred to as the main swarm, containing all particles As soon as a particle converges on a potential solution, a sub-swarm is created by grouping together particles that are in close proximity to the potential solution These particles are then removed from the main swarm, and continue within their sub-swarm to re ne (and to maintain) the solution Over time, the main swarm shrinks as sub-swarms are spawned from it NichePSO is considered to have converged when sub-swarms no longer improve the solutions that they represent The global best position from each sub-swarm is then taken as an optimum (solution) The NichePSO is summarized in Algorithm 1615 The di erent steps of the algorithm are explained in more detail in the following sections Algorithm 1615 NichePSO Algorithm Create and initialize a nx -dimensional main swarm, S; repeat Train the main swarm, S, for one iteration using the cognition-only model; Update the tness of each main swarm particle, Sxi ; for each sub-swarm Sk do Train sub-swarm particles, Sk xi , using a full model PSO; Update each particle s tness; Update the swarm radius Sk R; endFor If possible, merge sub-swarms; Allow sub-swarms to absorb any particles from the main swarm that moved into the sub-swarm; If possible, create new sub-swarms; until stopping condition is true; y Return Sk for each sub-swarm Sk as a solution;.

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The Executive Team needs to ensure that a directive against destruction of documents that are part of an investigation is written and distributed throughout the organization The team also needs to review and approve rules for managing, storing, preserving, and archiving electronic messages or other electronic data The rules should address the important issues, including listing the types of documents that are to be retained and how these documents are to be stored Lastly, the Executive Team needs to conduct routine audits of the Document Retention system generally on an unannounced basis The findings of these audits will provide the team with valuable insight into the quality of the current protocols, the degree to which staff members are in compliance, and what midcourse corrections would be necessary to achieve full compliance The days of the Mom and Pop nonprofit are over you have an obligation to your donors, your clients, your board, and your staff to ensure that your organization is in compliance with this component of SOX egislation It s not just a best practice, it s the law and it applies to all organizations in this country, including your nonprofit Exhibit 71 provides a SOX best practices checklist to help the Executive Team assess and improve its practices Technology Policy An important add-on to a Document Preservation policy is a Technology policy The Executive Team needs to design the policy to address the use of all types of technology that are present within the nonprofit, including e-mail, Internet access, voice mail, cell phones, laptops, PDAs, faxes, and other equipment owned by the nonprofit. Recognize Barcode In VS .NET Using Barcode decoder .Related: Print QR Code C# , ISBN Printing VB.NET , VB.NET Code 39 Generator

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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 ostalgic 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.

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