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The following three sets of folds will help crease the dotted lines and demonstrate a near-solution to the puzzle.
Particle-based arrays benefit from (a) "near-solution" kinetics, which means that the kinetics between a molecule bound to the surface of a particle and a free molecule equals those between two free molecules, (b) lower instrument-related costs, (c) higher sample throughput, and (d) good quality control by batch synthesis [ 34, 35].
This construction is done by matching a near source solution to a ray acoustics solution.
Local search is an algorithm chooses an optimal solution in the near solution space of the current solution, until it reaches a local optimal solution.
Moreover, we introduce a heuristic algorithm that adjusts any infeasibilities from the Lagrangian optimal solution to reach an optimal or a near optimal solution.
Finally, based on the characteristics of the objective function and feasible region of the decision variables, a heuristic solution procedure is developed to find a near optimal solution.
A near optimum solution to this problem is sought using a hybrid solution method integrating a discrete event simulation with a meta-heuristic search method.
The solution was improved using the genetic algorithm to find an optimum or a near optimum solution in the second stage.
However, the proposed algorithm gives a near optimal solution.
Particle swarm optimization is used to find a near optimal solution for the formulated problem.
Hence, we employ the methodology of Reinforcement Learning (RL) [2] to produce a near optimal solution.
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