An approach to stopping criteria for multi-objective optimization evolutionary algorithms: The MGBM criterion

Martí, Luis; García, J.; Berlanga, Antonio; Molina, José Manuel

doi:10.1109/cec.2009.4983090

Cited by 39 publications

(27 citation statements)

References 9 publications

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“…There are four clear situations where a MOEA should be stopped [14], to which we may add a fifth one regarding the concepts we have already introduced:…”

Section: Stopping Criteria In Moeasmentioning

confidence: 99%

“…There is a recent concern about obtaining general stopping criteria which can be applied to a wide range of algorithms and problems [3], [12], [13], [14], [19], dealing especially with industrial applications [20]. There are, as well, different non-general approaches: design of special algorithms which can guarantee local optimality of solutions [21], using as well the gradient of the hypervolume to guarantee diversity and spread [22] (which may be considered a transformation of the hypervolume into a progress indicator), or the design of algorithm specific stopping criterion, based on values used by the selection criterion [23] (in this reference the authors use the crowding distance for an NSGA-II [24] based stopping criterion).…”

Section: Global Stopping Criteriamentioning

confidence: 99%

“…There have been, as well, progress indicators specially designed for this purpose (instead of derived from quality ones), such as the mutual domination rate [13] (MDR), along with its associated stopping criterion, MGBM [14]. The main advantage of these synthesized progress indicators, opposed to the derived ones, is their low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Introducing a robust and efficient stopping criterion for MOEAs

Guerrero

Martí

Berlanga

et al. 2010

IEEE Congress on Evolutionary Computation

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IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Abstract-Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion.

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“…There are four clear situations where a MOEA should be stopped [14], to which we may add a fifth one regarding the concepts we have already introduced:…”

Section: Stopping Criteria In Moeasmentioning

confidence: 99%

Section: Global Stopping Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Introducing a robust and efficient stopping criterion for MOEAs

Guerrero

Martí

Berlanga

et al. 2010

IEEE Congress on Evolutionary Computation

Self Cite

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show abstract

“…Each experiment configuration was repeated 30 times in order to have statistically valid judgements. Algorithms runs were controlled by MGBM criterion [26].…”

Section: Methodsmentioning

confidence: 99%

Indicator-based MONEDA: A comparative study of scalability with respect to decision space dimensions

Martí

García

Berlanga

et al. 2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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“…The examination was done on the basis of a constrained single-objective power allocation problem using a particle swarm optimisation (PSO) algorithm. Marti et al (2007Marti et al ( , 2009) developed a stopping criterion for single and multiobjective optimisation problems which combines the mutual domination rate (MDR) improvement indicator, along with a simplified Kalman filter that is used as an evidence-gathering process. The MDR is aimed at tracking the progress of the optimisation with low computational cost and thus at solving single or multiobjective optimisation problems efficiently.…”

mentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

530

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. 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.

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An approach to stopping criteria for multi-objective optimization evolutionary algorithms: The MGBM criterion

Cited by 39 publications

References 9 publications

Introducing a robust and efficient stopping criterion for MOEAs

Introducing a robust and efficient stopping criterion for MOEAs

Indicator-based MONEDA: A comparative study of scalability with respect to decision space dimensions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

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