Dynamic multiobjective optimization problems: test cases, approximations, and applications

By Marco Farina, Kalyanmoy Deb, Paolo Amato. IEEE Transactions on Evolutionary Computation, 2004

673 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b852c56f98d77354650a980edef08f1ba5b07636

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Citations in the corpus

A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, background): “There have been a number of contributions made to several important aspects of this field, including dynamism classification [15], [41], test problems [4], [15], [20], [23]–[26], performance metrics [9], [15], [17]–[19], [41], [55], and algorithm design [9], [12], [15], [18], [21], [28], [54], [55].”
Evolutionary optimization in uncertain environments-a survey (2005, background): “problems has been considered in [72] and [73].”
Multiobjective cuckoo search for design optimization (2013, background): “Despite these challenges, multiobjective optimization has many powerful algorithms with many successful applications [1,4,10, 15,22,39,31,40,26].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, background): “An environmental change may involve the objective function (or functions if a dynamic multi-objective problem is considered [24, 25]), input variables, problem instances and constraints (e.g., dynamic constrained optimization [26]).”
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach (2006, methodology): “The FDA1 problem is defined as follows [14]: (3)”
Bat algorithm for multi-objective optimisation (2011, background): “Further to this complexity, a design problem is subjected to various design constraints, limited by design codes or standards, material properties and choice of available resources and costs (Deb, 2001; Farina et al., 2004).”
Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, background): “ONE OF the essential characteristics of dynamic multiobjective optimization problems (DMOPs) [1] is that objective functions will vary over time or under different environments.”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “, up to or larger than five), and those problems in dynamic [77], [78],”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, neutral): “And in [ 25 ] the case of dynamic multiobjective optimization, where the tness function changes over time, requiring a rapid re-optimization to track it, was tackled using a hybrid approach combining deterministic local search routines that are efcient in function evaluations, with a (1+1)-ES.”
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (2009, positive): “In the future, we plan to study the ability of MOEA/D for dealing with MOPs with many objectives, and those in uncertain and noisy environments [48], [49].”
Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling (2007, background): “Farina, Deb and Amato [5] proposed five dynamic test problems.”
Multiobjective firefly algorithm for continuous optimization (2012, background): “Further to this complexity, a design problem is subjected to various design constraints, limited by design codes or standards, material properties and the optimal utility of available resources and costs [2, 4].”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “According to the nature of the uncertainties in optimization, dynamic optimization problems can be classified into different categories [19], [20].”
A faster algorithm for calculating hypervolume (2006, background): “I. INTRODUCTION MULTIOBJECTIVE optimization problems abound, andmany evolutionary algorithms have been proposed to derive good solutions for such problems, for example [1]–[8].”
Fundamentals of natural computing: an overview (2007, background): “, [13,94,273]); investigations into evolutionary robotics, evolutionary hardware and evolutionary electronics [175,205,249,286]; biological applications and bioinformatics [17,103]; applications in data mining, games, arts and music [55,173,283]; and many others.”