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The piece-rate pay plan is used by companies that pay their employees at least in part based on the number of units of production completed To calculate wages under this method, multiply the rate paid per unit of production by the number of units completed in the work week An employer who uses this approach must still pay its staff for overtime hours worked; to calculate this, divide the total piece-rate pay by the hours worked, and then add the overtime premium to the excess hours worked An employer can avoid this extra calculation by computing wages earned during an overtime period by using a piece rate that is at least 15 times the regular piece rate

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The basic PSO is in uenced by a number of control parameters, namely the dimension of the problem, number of particles, acceleration coe cients, inertia weight, neighborhood size, number of iterations, and the random values that scale the contribution of the cognitive and social components Additionally, if velocity clamping or constriction is used, the maximum velocity and constriction coe cient also in uence the performance of the PSO This section discusses these parameters The in uence of the inertia weight, velocity clamping threshold and constriction coe cient has been discussed in Section 163 The rest of the parameters are discussed below: Swarm size, ns , ie the number of particles in the swarm: the more particles in the swarm, the larger the initial diversity of the swarm provided that a good uniform initialization scheme is used to initialize the particles A large swarm allows larger parts of the search space to be covered per iteration However, more particles increase the per iteration computational complexity, and the search degrades to a parallel random search It is also the case that more particles may lead to fewer iterations to reach a good solution, compared to smaller swarms It has been shown in a number of empirical studies that the PSO has the ability to nd optimal solutions with small swarm sizes of 10 to 30 particles [89, 865] Success has even been obtained for fewer than 10 particles [863] While empirical studies give a general heuristic of ns [10, 30], the optimal swarm size is problem-dependent A smooth search space will need fewer particles than a rough surface to locate optimal solutions Rather than using the heuristics found in publications, it is best that the value of ns be optimized for each problem using cross-validation methods Neighborhood size: The neighborhood size de nes the extent of social interaction within the swarm The smaller the neighborhoods, the less interaction occurs While smaller neighborhoods are slower in convergence, they have more reliable convergence to optimal solutions Smaller neighborhood sizes are less susceptible to local minima To capitalize on the advantages of small and large neighborhood sizes, start the search with small neighborhoods and increase the neighborhood size proportionally to the increase in number of iterations [820] This approach ensures an initial high diversity with faster convergence as the particles move towards a promising search area Number of iterations: The number of iterations to reach a good solution is also problem-dependent Too few iterations may terminate the search prematurely A too large number of iterations has the consequence of unnecessary added computational complexity (provided that the number of iterations is the only stopping condition) Acceleration coefficients: The acceleration coe cients, c1 and c2 , together with the random vectors r1 and r2 , control the stochastic in uence of the cognitive and social components on the overall velocity of a particle The constants c1 and c2 are also referred to as trust parameters, where c1 expresses how much.

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