Evolutionary memetic algorithms supported by metaheuristic profiling effectively applied to the optimization of discrete routing problems

Wood, David A.

doi:10.1016/j.jngse.2016.09.031

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…The effectiveness of each meme in contributing to a solution is monitored by a metaheuristic profiling technique [36,37], a graphical representation of which is illustrated by Fig. 4 for an optimization solution derived for Case 5C.…”

Section: End Whilementioning

confidence: 99%

“…• Allocating records to the tuning, testing and training subsets: ~ 3.5 s • Identifying and ranking squared differences of each tuning and testing subset data record against all data records in the training subset (i.e., more than 9250 data records matched for each tuning and testing subset record) ~ 168 s • Calculating TOB stage 1 predictions: ~ 0.5 s • Executing TOB Stage 2 optimizers (Solver GRG, Solver evolutionary, memetic firefly) to train tuning subsets, consisting of ~ 150 data records, to reach convergence at an RMSE minimum (actual versus predicted PE for all data records in the tuning subsets); or be terminated after a specified running time: [36,37] for the first 110 iterations of the optimizer (Case #5C). The top-ten solutions found by each meme are distinguished top-10 best data matches from the large training subset is the most computationally expensive.…”

Section: Computational Performancementioning

confidence: 99%

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Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Wood

2020

SN Appl. Sci.

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Electrical power output (PE) for a combined cycle gas turbine (CCGT) consisting of 9568 data records collected over a 6-year period is evaluated by the transparent open box (TOB) machine-learning method to provide accurate PE predictions and insight to prediction errors. The PE predictions derived by applying the TOB optimized data matching technique are more accurate than published predictions for the dataset from fifteen correlation-based, machine-learning algorithms. TOB achieves this high-accuracy using a tuning subset of < 150 (~ 1.5%) data records. Its accuracy is confirmed by testing the optimized solutions against all dataset records in 15 runs spread across five shuffled datasets. The dataset has a few extreme outliers associated with its four independent variables and these negatively impact the prediction accuracy of machine-learning methods. Through its transparency and forensic-like auditability of its calculations for individual data records, the TOB algorithm is able to mine the dataset to provide useful insight to the interactions of the outliers with other data records. This enables a filtered dataset (9533 records), excluding 35 carefully selected data records, to be customized to deliver much improved prediction accuracy (RMSE = 2.89 MW). Mining the dataset also reveals significant differences in prediction accuracy achieved for different sectors of the PE distribution. This insight identifies that prediction accuracy could be further improved by dividing the dataset into separately optimized subsets, three along its main PE trend plus a fourth, small subset consisting of the outliers. The TOB algorithm demonstrates its value as a machine-learning tool capable of generating accurate predictions and easily auditable data mining. It is well suited for CCGT efficiency and performance optimization.

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Section: End Whilementioning

confidence: 99%

Section: Computational Performancementioning

confidence: 99%

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Wood

2020

SN Appl. Sci.

Self Cite

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show abstract

“…The effectiveness of each meme in contributing to a solution is monitored by a metaheuristic profiling technique [62,63], a graphical representation of which is illustrated by Fig. 6 for an optimization solution derived for Case#12.…”

Section: Tob Stage 2 (Optimizing the Weights And Number Of Matching Rmentioning

confidence: 99%

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Wood

2020

Technol Econ Smart Grids Sustain Energy

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The stability of decentralized electricity grids is influenced by real-time electricity prices and the cost sensitivity and reaction times of power producers and consumers. The decentral smart grid control (DSGC) system is designed to provide demand-side control of decentralized electricity grids by linking real-time electricity prices to changes in grid frequency over the time scale of a few seconds. This stimulates electricity demand-side consumption / production on similar time scales. Grid stability of DSGC systems can be simulated by considering a wide range of assumptions for the electricity volumes consumed / produced (P) by each grid participant, their cost-sensitivity (G) and reaction times (Tau) to changing grid conditions. Such a simulation (10,000 cases) published for a simple four-node star decentralized grid configuration with randomized values for P, G and Tau quantifies dynamic grid stability (Stb in) in terms of grid mechanical and pricing influences. This study applies an optimized data-matching machine-learning algorithm, the, transparent open box (TOB) learning network to predict Stb in (ranging from −0.0808 to +0.1094 s −2) for this published simulation from its independent variables. TOB manages to predict Stb in to a high degree of accuracy (RMSE~0.016 s −2 ; R 2~0 .85) for this grid configuration in which independent variables P, G and Tau are poorly correlated with Stb in. By involving average G and Tau values for the three consumers as input variables TOB prediction accuracy is further improved (RMSE~0.0075 s −2 ; R 2~0 .90). The study highlights the importance of compound feature selection when predicting grid stability of decentralized electricity grids.

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“…The obtained detector data formed clones optimized by the CSA and had constant memory, improving classification accuracy and reducing run time [10]. Evolutionary memetic algorithms were effectively applied to optimize discrete routing problems, such as the traveling salesman problem, and involved the use of memetic algorithm, metaheuristic profile, and trajectory-based optimization methods [11][12][13][14][15][16][17][18]. With the integration of machine learning [19][20][21][22][23][24][25][26], evolutionary models can leverage advanced machine learning techniques to enhance their capabilities.…”

Section: Introductionmentioning

confidence: 99%

Design a wing flange lever mechanism using Giza pyramid algorithm

Al-Shabi

Abouelazm

Nassif

et al. 2023

Sensors and Systems for Space Applications XVI

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In this work, a well-known mechanism that is referred to as piston lever mechanism is design to control the wing's flange. The design targets the mechanism components and their location while maintaining the minimum oil level required to lift the flanges from 0 to 45 degrees. The design is considered a multi-objective optimization problem; in which we propose the Giza Pyramid Algorithm (GPA) to optimize. GPA is a newly developed metaheuristic technique of type ancientinspired that was developed in 2021. The GPA shall obtain the best design while satisfying four constrains. The algorithm will be tested in Monte Carlo simulation to show its effectiveness in terms of stability and robustness. The performance is then compared to the particle swarm optimization. The proposed method shows a superior result compared to the PSO.

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Evolutionary memetic algorithms supported by metaheuristic profiling effectively applied to the optimization of discrete routing problems

Cited by 12 publications

References 33 publications

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Design a wing flange lever mechanism using Giza pyramid algorithm

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