Selected Aspects of Natural Computing

Corne, David; Deb, Kalyanmoy; Knowles, Joshua; Yao, Xin

doi:10.1007/978-3-540-92910-9_52

Cited by 4 publications

(4 citation statements)

References 86 publications

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“…In particular, the hyperheuristics [160], [161], which is also termed as superheuristics [162], represent a hierarchical hybridization framework, which employs a highlevel methodology (e.g., machine-learning techniques and evolutionary algorithms) to schedule a set of low-level heuristics. Different low-level heuristics will be applied at any given time, depending on the current problem state or search stage [161].…”

Section: ) Cc?? Needs To Be More Exploredmentioning

confidence: 99%

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Xin

Chen

Zhang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

162

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Abstract-Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.

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Section: ) Cc?? Needs To Be More Exploredmentioning

confidence: 99%

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Xin

Chen

Zhang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

162

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“…Evolutionary algorithms (EAs) are state-of-the-art, efficient heuristic search methods that solve complex optimization problems by employing the Darwinian theory of natural selection. Due to their flexibility to capture global solutions and ability to self adapt, evolutionary algorithms have been applied to various real-world problems in areas such as engineering [2,6], supply chain management [7,9] and communication networks [15,18].…”

Section: Introductionmentioning

confidence: 99%

Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks

Neumann,

Gounder,

Yan

et al. 2023

Proceedings of the Genetic and Evolutionary Computation Conference

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Otherwise known as "Self-Archiving" or "Posting Rights", all ACM published authors of magazine articles, journal articles, and conference papers retain the right to post the presubmitted (also known as "pre-prints"), submitted, accepted, and peer-reviewed versions of their work in any and all of the following sites: • Author's Homepage • Author's Institutional Repository • Any Repository legally mandated by the agency or funder funding the research on which the work is based • Any Non-Commercial Repository or Aggregation that does not duplicate ACM tables of contents. Non-Commercial Repositories are defined as Repositories owned by nonprofit organizations that do not charge a fee to access deposited articles and that do not sell advertising or otherwise profit from serving scholarly articles.

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“…In this work, the dynamic characteristics of a new ASG driver circuit were optimized using the TFT-circuitsimulation-based multi-objective evolutionary algorithm (MOEA) [12][13][14][15][16], which works on the unified optimization framework (UOF) [17]. As shown in Figure 1, the ASG driver circuit with double-ended outputs optimized the widths of devices to improve the rise time, fall time, and ripple peak.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of a novel amorphous silicon gate driver circuit using a TFT-circuit-simulation-based multi-objective evolutionary algorithm

Hung

Chiang

et al. 2016

Journal of Information Display

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Optimal design of a novel amorphous silicon gate driver circuit using a TFT-circuit-simulationbased multi-objective evolutionary algorithm, Journal of Information Display, 17:2, 51-58, DOI: 10.1080/15980316.2016 A short rise time, short fall time, and small ripple are required to reduce the misoperation of pixel data voltage and to improve the stable signal processing of a driver circuit. In this study, a novel amorphous silicon gate (ASG) driver circuit consisting of 15 hydrogenated amorphous silicon thin-film transistors (a-Si:H TFTs) and two capacitors was optimized using a thin-film transistor (TFT)-circuitsimulation-based multi-objective evolutionary algorithm on the unified optimization framework . The ASG circuit was optimized for the following given specifications: rise time < 0.7 µs; fall time < 0.6 µs; ripple peak < 6.5 V; clock Ctotal < 40 pf; and total TFT widths < 6000 µm. The main findings of this study show that the rise time had an 18% reduction and that the fall time, total widths, and clock Ctotal had 7, 17.5, and 9% reductions, respectively. ARTICLE HISTORY

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Selected Aspects of Natural Computing

Cited by 4 publications

References 86 publications

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks

Optimal design of a novel amorphous silicon gate driver circuit using a TFT-circuit-simulation-based multi-objective evolutionary algorithm

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