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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
[4] [11] High object speeds have a similar effect—faster objects are harder to track, and humans are completely unable to track objects that move sufficiently fast. This "speed limit", however, is much slower than the maximum object speed at which humans can judge the object's movement direction.
Multi-objective linear programming is a subarea of mathematical optimization. A multiple objective linear program (MOLP) is a linear program with more than one objective function. An MOLP is a special case of a vector linear program. Multi-objective linear programming is also a subarea of Multi-objective optimization.
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. [ 1 ] [ 2 ] It is generally divided into two subfields: discrete optimization and continuous optimization .
In calculus, the racetrack principle describes the movement and growth of two functions in terms of their derivatives.. This principle is derived from the fact that if a horse named Frank Fleetfeet always runs faster than a horse named Greg Gooseleg, then if Frank and Greg start a race from the same place and the same time, then Frank will win.
Fast Track (American TV series), a 1997–1998 action drama series; Fast Track (British TV programme), a 2013–2014 travel programme; Fast Track, a FoxBusiness.com show formerly hosted by Anna Gilligan; The Ex (working title Fast Track), an American comedy; Fast Track: No Limits, a 2008 English-language German thriller film
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A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.