Journal ArticleDOI
Borg: An auto-adaptive many-objective evolutionary computing framework
David Hadka,Patrick M. Reed +1 more
TLDR
The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework for many-objective, multimodal optimization.Abstract:
This study introduces the Borg multi-objective evolutionary algorithm MOEA for many-objective, multimodal optimization. The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameteri-zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.read more
Citations
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Journal ArticleDOI
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.
Journal ArticleDOI
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
Himanshu Jain,Kalyanmoy Deb +1 more
TL;DR: This paper extends NSGA-III to solve generic constrained many-objective optimization problems and suggests three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many- objective optimizer.
Journal ArticleDOI
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
TL;DR: In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space, and reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization.
Journal ArticleDOI
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Xingyi Zhang,Ye Tian,Yaochu Jin +2 more
TL;DR: A knee point-driven EA to solve MaOPs by showing that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given and enhancing the convergence performance in many-objective optimization.
Journal ArticleDOI
Evolutionary algorithms and other metaheuristics in water resources
Holger R. Maier,Zoran Kapelan,Joseph R. Kasprzyk,Joshua B. Kollat,L. S. Matott,Maria da Conceição Cunha,Graeme C. Dandy,Matthew S. Gibbs,Edward Keedwell,Angela Marchi,Avi Ostfeld,Dragan Savic,Dimitri Solomatine,Jasper A. Vrugt,Aaron C. Zecchin,Barbara S. Minsker,Emily Barbour,George Kuczera,F. Pasha,Andrea Castelletti,Matteo Giuliani,Patrick M. Reed +21 more
TL;DR: Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.
References
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Related Papers (5)
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more