Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Filatovas, Ernestas; Kurasova, Olga; Sindhya, Karthik

doi:10.15388/informatica.2015.37

Cited by 29 publications

(10 citation statements)

References 62 publications

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“…Artificial intelligence methods are under wide development in various directions. These developments cover artificial neural networks (Haykin, 2009;Schmidhuber, 2011;Dzemyda et al, 2007Dzemyda et al, , 2013Medvedev et al, 2011;Ivanikovas et al, 2011), evolutionary computation (Simon, 2013;Eiben and Smith, 2003;Filatovas et al, 2015;Kurasova, 2013, 2014), fuzzy set theory (Ross, 2010), artificial immune systems (Al-Enezi et al, 2010), etc. Some taxonomy of nature inspired artificial intelligence is given in Goel et al (2012).…”

Section: Swarm Intelligencementioning

confidence: 99%

Swarm Intelligence for Frequency Management in Smart Grids

Evora¹,

Hernández²,

Hernández³

et al. 2015

Informatica

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A secure and high-quality operation of power grids requires frequency to be managed to keep it stable around a reference value. The deviation of the frequency from this reference value is caused by the imbalance between the active power produced and consumed. In the Smart Grid paradigm, the balance can be achieved by adjusting the demand to the production constraints, instead of the other way round. In this paper, an swarm intelligence-based approach for frequency management is proposed. It is grounded on the idea that a swarm is composed of decentralised individual agents (particles) and that each of them interacts with other ones via a shared environment. Three swarm intelligence-based policies ensure a decentralised frequency management in the smart power grid, where agents of swarm are making decisions and acting on the demand side. Policies differ in behaviour function of agents. Finally, these policies are evaluated and compared using indicators that point out their advantages.

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Section: Swarm Intelligencementioning

confidence: 99%

Swarm Intelligence for Frequency Management in Smart Grids

Evora¹,

Hernández²,

Hernández³

et al. 2015

Informatica

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“…In [22] an interactive algorithm based on R-NSGA-II is proposed, and in [23] R-NSGA-II is modified by integrating a stochastic local search in a memetic fashion, see [47], [10], [32], and [72].…”

Section: Progressive Preference Articulation: a Brief Reviewmentioning

confidence: 99%

Progressive preference articulation for decision making in multi-objective optimisation problems

Rostami

Neri

Epitropakis

2017

ICA

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Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.

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“…Among EMO algorithms considering preferences in the optimization process, apart from preference-based algorithms (which consider preferences expressed by the DM a priori before the optimization takes place), we can also find the so-called progressively interactive EMO algorithms, which interact with the DM along the algorithm run for a progressive elicitation of the preferences. In the literature, there are many preference-based EMO algorithms, such as the ones proposed in Deb et al (2006), Deb and Jain (2014), Lopez-Jaimes and Coello (2014), Filatovas, Kurasova, and Sindhya (2015), and Ruiz et al (2015b), among others. Examples of interactive EMO algorithms can be found in Gong, Liu, Zhang, Jiao, andZhang (2011), Wang, Purshouse, andFleming (2013), Brockhoff, Hamadi, and Kaci (2014), Sinha, Korhonen, Wallenius, and Deb (2014), and Chugh, Sindhya, Hakanen, and Miettinen (2015).…”

Section: Related Workmentioning

confidence: 99%