Multiobjective Optimization: Principles and Case Studies

By Y. Collette, Patrick Siarry. 2004

777 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Control and Systems Engineering

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Citations in the corpus

Learning Fair Rule Lists (2019, background): “A multi-objective optimization problem [15, 22] can be defined as follows: in which V is the set of decision variables, m ≥ 2 the number of objective functions, z i the i th objective function and Ω represents the set of feasible solutions.”
A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem (2008, methodology): “Much work has been devoted to the subject of multi-objective problems, and the developed methods to solve multi-objective optimization can be generally classified into five different types (Collette and Siarry 2003 ): (1) scalar methods; (2) interactive methods; (3) fuzzy methods; (4) metho”
Design of a Fractional Order Frequency PID Controller for an Islanded Microgrid: A Multi-Objective Extremal Optimization Method (2017, background): “The Laplace transform of Equation (1) is defined as the following form: ∫ ∞ 0 e−staDrt f (t)dt = s rF(s)− n−1 ∑ k=0 sk0Drt f (t)|t=0 (2) Figure 1 shows a block diagram of a closed-loop control system with a FOPID controller called a PIλDµ controller [17].”
Bayesian Optimization for Adaptive Experimental Design: A Review (2020, background): “Pareto set of optimal points can be found [105].”
Recycling Privileged Learning and Distribution Matching for Fairness (2017, background): “While solving a single-objective optimization implies to search for a single best solution, a collection of solutions at which no goal can be improved without damaging one of the others (Pareto frontier) [40] is sought when solving a multi-objective optimization.”
Pareto efficient multi-objective test case selection (2007, background): “We believe that such an approach is well suited to the regression test case selection problem, because it is likely that a tester will want to optimize several possible conﬂicting constraints.”
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, methodology): “The first and last aspects could be evaluated through classical performance indexes used for classical MOEAs (the spacing and hole-relative-size [91] performance measures were adopted here), and new performance measures were developed by the authors to account for the cluster and rank-order preserva”
Bio-inspired computation: Where we stand and what's next (2019, neutral): “The old days in this subarea of bio-inspired computation focused on two main strategies to deal with multiple objectives [117,118]: 1) non-elitist non-Pareto-based methods,”
Preference-based Pareto optimization in certain and noisy environments (2009, neutral): “Examples of results for Harrington’s DFs and the Binh problem The Binh problem (Collette and Siarry 2003, p. 49) consists of two objective functions f1 and f2, which depend on two parameters x1 and x2: Minimize f1(x1, x2) = x21 + x22 (4) Minimize f2(x1, x2) = (x1 − 5)2 + (x2 − 5)2 (5) with − 5 ≤ x1…”
Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis (2007, neutral): “Multicriteria decision-making being outside the scope of this paper, we refer the reader to Collette and Siarry (2004) for further details on the subject.”
Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach (2014, background): “The efficient (or Pareto optimal, non-dominated, non-inferior) solutions are the solutions that cannot be improved in one objective function without deteriorating their performance in at least one of the rest [39].”