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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Proceedings ArticleDOI
Optimization of economic load dispatch for a microgrid using evolutionary computation
TL;DR: In this article, two state-of-the-art multi-objective methods, strength pareto evolutionary algorithm 2 (SPEA2) and non-dominated sorting genetic algorithm (NSGA-II), are adopted to perform the optimization.
Journal ArticleDOI
A repository of real-world datasets for data-driven evolutionary multiobjective optimization
TL;DR: This work carefully selects seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjectives optimization by suggesting a set of benchmark problems extracted from various real- world optimization applications.
Journal ArticleDOI
Efficient optimization of many objectives by approximation-guided evolution
TL;DR: This paper presents a framework for evolutionary multi-objective optimization that allows to work with a formal notion of approximation, and compares AGE with two additional algorithms that use very fast hypervolume-approximations to guide their search.
Journal ArticleDOI
EASEA: specification and execution of evolutionary algorithms on GPGPU
TL;DR: EASEA is a framework designed to help non-expert programmers to optimize their problems by evolutionary computation that allows to generate code targeted for standard CPU architectures, GPGPU-equipped machines as well as distributed memory clusters.
Journal ArticleDOI
A Subregion Division-Based Evolutionary Algorithm With Effective Mating Selection for Many-Objective Optimization
TL;DR: The proposed SdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 23 test problems from DTLZ, WFG, and MaF test suites and experimental results demonstrate its effectiveness on improving the performance of the embedded algorithms.