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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.
In multi-objective optimization, the Pareto front (also called Pareto frontier or Pareto curve) is the set of all Pareto efficient solutions. [1] The concept is widely used in engineering . [ 2 ] : 111–148 It allows the designer to restrict attention to the set of efficient choices, and to make tradeoffs within this set, rather than ...
In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems (MOP) are given. The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3]
The Pareto front consists of all Pareto-efficient situations. [2] ... In the multi-objective optimization setting, various solutions can be "incomparable" ...
The Interactive Decision Maps technique of multi-objective optimization is based on approximating the Edgeworth-Pareto Hull (EPH) of the feasible objective set, that is, the feasible objective set broadened by the objective points dominated by it. Alternatively, this set is known as Free Disposal Hull.
Reward-based selection can be used within Multi-armed bandit framework for Multi-objective optimization to obtain a better approximation of the Pareto front. [1]The newborn ′ (+) and its parents receive a reward (), if ′ (+) was selected for new population (+), otherwise the reward is zero.
Multi-objective optimization methods usually generate a so-called „Pareto front“ or use a weighting function to generate a single Pareto point. Based on the search methods, Optimus optimization methods (both single and multi-objective) can be categorized into: local optimization methods - searching for an optimum based on local information ...
"Particle swarm optimization for feature selection in classification: a multi-objective approach". ... "Pareto front feature selection based on artificial bee colony ...