Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy

Deng, Libao; Li, Chunlei; Lan, Yanyan; Sun, Gaoji; Shang, Changjing

doi:10.1016/j.eswa.2021.116298

Cited by 24 publications

(8 citation statements)

References 65 publications

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“…This paper incorporates PSE into differential evolution(DE) [55] as a comparative algorithm. 7) DCDE [56]: DCDE is a novel variant of DE, based on dynamic composite mutation operators and a twolevel parameter adjustment strategy. DCDE demonstrates strong performance in optimizing global prob-lems, and this paper utilizes it as a benchmark algorithm for comparative analysis.…”

Section: B Comparison Algorithmsmentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

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Section: B Comparison Algorithmsmentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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“…Numerous self-adaptive parameter approaches are proposed in the literature [33][34][35][36] to solve different optimization problems. Algorithmic simplicity, ease of implementation, and strong convergence have inspired many researchers to employ DE to solve various optimization problems.…”

Section: The Classical Dementioning

confidence: 99%

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

Raghul,

Jeyakumar

2023

IEEE Access

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In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, overpartitioning the population may lead to aberrant utilization of computational resources among the subpopulations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and reinitialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the hybrid multi-population reinitialization strategy outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.

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“…Carried out as a part of the current paper, a review on recent publications which propose new or improved DE variants revealed that only a small proportion of papers consider the strategy of dealing with infeasible solutions as a mechanism influencing (Deng, 2020), (Cheng et al, 2021), (Mohamed et al, 2021): reinitialization; (Liu et al, 2019), (Stanovov et al, 2020): HVB; , (Zhan et al, 2020), (Zhao et al, 2020): saturation; (Deng et al, 2022): midpoint-base.…”

Section: State-of-the-art On Strategies Of Dealing With Infeasible So...mentioning

confidence: 99%

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

Kononova¹,

Vermetten²,

Caraffini³

et al. 2022

Preprint

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We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours -in terms of performance, disruptiveness and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants on special test function f0 and BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem's dimensionality. Different Evolution is not at all special in this regard -there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the field of heuristic optimisation to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we call here a strategy of dealing with infeasible solutions. This component needs to be consistently (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on algorithm's performance in a wider sense and (c) included in the (automatic) algorithmic design. All of these should be done even for problems with box constraints.

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Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy

Cited by 24 publications

References 65 publications

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

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