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The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue).
This highlights the need to understand the extent to which perceptual development in general may be described as optimization of inference.
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Finally, the unbiased H∞ filtering problems are described as optimization algorithms.
The boundedness criteria are formulated in the form of linear matrix inequalities and the designed algorithms are described as optimization ones.
In [15, 16], under a simple assumption of normality, the L 0-optimal control is proved to be equivalent to classical L 1-optimal (or fuel optimal) control, which can be described as a convex optimization.
As a result, the capability can be described as a nonlinear optimization problem.
The dimensional synthesis can be described as the following optimization problem: begin{array}{*{20}l} { hbox{min} } hfill & {l_{1} + l_{2} + l_{3} } hfill {{text{s}}.
The CTCOVRP can therefore be described as the following optimization problem: Objective function: Minimize the number of vehicles.
We show that this problem can be described as a graph-theoretical optimization problem.
The movements control in C i can be described as the following Multi-objective Optimization Problem (MOP): min f 1 ( x ̄ ) = 1 - P cov ( S i ) f 2 ( x ̄ ) = ∑ i ∈ S i l i ′ - l i s. t. l i ′ - l i ≤ d t h ( i - 1 ) * R s ≤ l i ′ - l sin k ≤ i * R s (24).
The resulting optimization can be described as a linear program, and hence we can solve it efficiently by using numerical software such as CVX in MATLAB [26, 27].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com