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Although the basic PSO was developed to nd single solutions to optimization problems, it has been observed for PSO implementations with special parameter choices that the basic PSO has an inherent ability to nd multiple solutions Agra otis and Cede o, for example, observed for a speci c problem that particles coalesce into a n small number of local minima [11] However, any general conclusions about the PSO s niching abilities have to be reached with extreme care, as explained below The main driving forces of the PSO are the cognitive and social components It is the social component that prevents speciation Consider, for example, the gbest PSO The social component causes all particles to be attracted towards the best position obtained by the swarm, while the cognitive component exerts an opposing force (if the global best position is not the same as a particle s personal best position) towards a particle s own best solution The resulting e ect is that particles converge to a point between the global best and personal best positions, as was formally proven in [863, 870] If the PSO is executed until the swarm reaches an equilibrium point, then each particle will have converged to such a point between the global best position and the personal best position of that particle, with a nal zero velocity Brits et al [91] showed empirically that the lbest PSO succeeds in locating a small percentage of optima for a number of functions However, keep in mind that these studies terminated the algorithms when a maximum number of function evaluations was exceeded, and not when an equilibrium state was reached Particles may therefore still have some momentum, further exploring the search space It is shown in [91] that the basic PSO fails miserably compared to specially designed lbest PSO niching algorithms Of course, the prospect of locating more multiple solutions will improve with. asp.net pdf viewer annotation Review and print PDF with ASP . NET Web Forms PDF Viewer ...
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A simple PDF Viewer that allows you to be able to view, print and extract the contents of your pdf file in just a few clicks. You can... Expand ▾. 1 Review. An employer may allow its employees to either purchase assets from the company or through it In the first case, the company may be liquidating assets and offers to sell them to its employees In the latter case, employees are allowed to use the company s bulk-purchase discounts in order to obtain items at reduced prices from other suppliers The company may also sell its own products at reduced prices to employees through a company store Whatever the reason for the purchase, some asset purchases are so large that employees are unable to pay back the company immediately for the full amount of the purchase Instead, the company allows them to pay back the company through a series of payroll deductions When this happens, an employee should sign an agreement with the company, acknowledging responsibility for paying back the company and also agreeing to a specific payment schedule Though not common, the company can also charge the employee an interest rate, which may encourage the person to pay back the company somewhat sooner in order to avoid an excessive interest expense For long repayment schedules, it may be useful to keep employees apprised of the remaining amount of each loan, so the payroll staff should consider either maintaining a separate schedule of payments or creating a loan goal through its payroll software that tracks the amount of the goal that has not yet been reached java ean 13 reader, code 128 barcode add in for microsoft word, java code 39 reader, word data matrix code, barcode generator excel mac, java barcode reader example download asp.net pdf viewer annotation ASP . NET PDF Editor: view, create, convert, annotate , redact, edit ...
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NET , WPF, WEB | PDF MRC Compression Library. ... Reader , Writer and Editor of PDF documents for . NET , WPF and .... Create and edit PDF annotations of PDF document .... The SDK comes with demo applications for WinForms, WPF, ASP . 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;. asp.net pdf viewer annotation ASP . NET component that allows online Annotation of PDF files ...
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