Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

Peraza-Vázquez, Hernán; Torres‐Huerta, A.M.; Flores‐Vela, A.

doi:10.13053/cys-20-2-2334

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…To create a trial vector against the given vector, a mutation strategy from a successful experience-based pool of mutation strategies and control parameters F and CR are updated by normal distribution and Cauchy distribution. A hyper heuristic based self-adaptive DE is introduced by [40] for mixed integer non-linear programming problems in their research work. This algorithm used e-constrained for handling constraint and self-adaptive parameters, a number of crossover and mutation strategies, and normal distribution-based control parameters F and CR.…”

Section: Literature Surveymentioning

confidence: 99%

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Abbas¹,

Malik²,

Saudagar³

et al. 2022

Computers, Materials &Amp; Continua

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One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.

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Section: Literature Surveymentioning

confidence: 99%

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Abbas¹,

Malik²,

Saudagar³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…DE was initially developed to solve global optimization problems in continuous spaces [10] and it quickly became a widely used optimization algorithm due to its characteristics of fast convergence and high capacity for exploration of feasible solutions to hard optimization problems [11]- [14].…”

Section: Introductionmentioning

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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Solving Continuous Optimization Problems with a New Hyperheuristic Framework

Bándi,

Gaskó

2024

Lecture Notes in Computer Science

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Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

Cited by 6 publications

References 38 publications

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

A Micro-Differential Evolution Algorithm for Continuous Complex Functions

Solving Continuous Optimization Problems with a New Hyperheuristic Framework

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