Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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Journal ArticleDOI
Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization
TL;DR: MOPSO integrating with variable neighborhood search is introduced to address FJSP efficiently and three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability.
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AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction
TL;DR: A three-objective evolution algorithm called AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search, is proposed and demonstrated by the performance comparison with other five state-of-the-art PSP methods.
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Modular design space exploration framework for embedded systems
TL;DR: A generic approach is described based on multi-objective decision making, black-box optimisation and randomised search strategies, which resolves the problem that existing optimisation methods cannot be coupled easily to the problem-specific part of a design exploration tool.
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Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)
TL;DR: The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions.
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GALE: Geometric Active Learning for Search-Based Software Engineering
TL;DR: GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier that finds comparable solutions to standard methods using far fewer evaluations.