Micro Differential Evolution Performance Empirical Study for High Dimensional Optimization Problems

Carbajal, Mauricio Olguín; Lozada, Juan Carlos Herrera; Arellano‐Verdejo, Javier; Barrón-Fernández, Ricardo; Taud, Hind

doi:10.1007/978-3-662-43880-0_31

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Since then, several micropopulation Evolutionary Algorithms (μ EAs) followed, such as, e.g., [83]. After understanding the potential of a classic micro-Diferential Evolution (μ DE) over large-scale problems, several μ DE schemes, self-adaptive variants (such as μ JADE), and hybrid memetic alterations were proposed [75,78,[84][85][86][87][88][89][90][91]. Analogously, Swarm Intelligence algorithms have been shown to have similar advantages when run with micropopulations.…”

Section: Micropopulationsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

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Section: Micropopulationsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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“…x o ← x 1;2k+1 15: end while 16 . To find a better approximation of the minimizer, the length r is reduced by the factor k if x 1 is not at the ends of the considered ray (line 13).…”

Section: Algorithm 1 Line Searchmentioning

confidence: 99%

A Micro-Differential Evolution Algorithm for Continuous Complex Functions

et al. 2019

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In this paper, we incorporate a local search procedure into a micro differential evolution algorithm MED with the aim of solving the HappyCat function. Our purpose is to find out if our proposal is more competitive than a Ray-ES algorithm. We test our micro Differential Evolution algorithm (µDE) on HappyCat and HGBat functions. The results that we obtained with micro-DE are better compared with the results the original RayES reference algorithm. This analysis supports our conjecture that a reduced population DE hybridized with a local search (Ray search) is a key combination in dealing with this function. Our results support the hypothesis that a well-focused micro population is more accurate and efficient than existing techniques, representing (that of micro-algorithms) a serious competitor because of its efficiency and accuracy. In fact, the proposed (but never solved) HGBat function can be dealt with, showing the scalability and potential future uses of our technique. INDEX TERMS Highly difficult problems, hybrid algorithms, HappyCat, HGBat, RayES, micro-algorithms. M. OLGUÍN-CARBAJAL received the degree in communications and electronics engineering from Culhuacan Unit, Higher School of Mechanical and Electrical Engineering (ESIME), National Polytechnic Institute, México, in 1995, the master's degree in computer engineering, in 2001, and D.Sc. degree from the Center for Computer Research (CIC), in 2011. In 2013, he did the Postdoctoral Residency with the University of Málaga, Spain. He has been a Professor and a full-time Researcher with the Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), since 2005. He has published several articles related to intelligent computing, computer graphics, virtual reality, and electronic applications in mobile devices.

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“…Instead of relying on direct numerical comparison, Li et al 9 12 also applied the Friedman and post hoc tests to compare a micro DE with only five candidate solutions (NP = 5), an adjusted DE for high problem dimensionality, and a standard DE variant ( ) using the same F for all algorithms. Both of these works performed extensive empirical comparisons.…”

Section: Related Workmentioning

confidence: 99%

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Wong¹,

Bigras²,

Duchaîne³

et al. 2016

IJCIS

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To be cost-effective, most commercial off-the-shelf industrial controllers have low system order and a predefined internal structure. When operating in an industrial environment, the system output is often specified by a reference model, and the control system must closely match the model's response. In this context, a valid controller design solution must satisfy the application specifications, fit the controller's configuration and meet a model matching criterion. This paper proposes a method of solving the design problem using bilinear matrix inequality formulation, and the use of Differential Evolution (DE) algorithms to solve the resulting optimization problem. The performance of the proposed method is demonstrated by comparing a set of ten DE variants. Extensive statistical analysis shows that the variants and are effective in terms of mean best objective function value, average number of function evaluations, and objective function value progression.

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Micro Differential Evolution Performance Empirical Study for High Dimensional Optimization Problems

Cited by 4 publications

References 4 publications

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

A Micro-Differential Evolution Algorithm for Continuous Complex Functions

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

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