An ensemble learning algorithm for optimization of spark ignition engine performance fuelled with methane/hydrogen blends

2024-11-19T12:41:37+00:00

On this week's Journal Club session, Mohammad Tayaraninajaran will talk about his paper "An ensemble learning algorithm for optimization of spark ignition engine performance fuelled with methane/hydrogen blends".

The increasing global demand for sustainable and cleaner transportation has led to extensive research on alternative fuels for Internal Combustion (IC) engines. One promising option is the utilization of methane/hydrogen blends in Spark-Ignition (SI) engines due to their potential to reduce Green House Gas (GHG) emissions and improve engine performance. However, the optimal operation of such an engine is challenging due to the interdependence of multiple conflicting objectives, including Brake Mean Effective Pressure (BMEP), Brake Specific Fuel Consumption (BSFC), and nitrogen oxide (NOx) emissions. This paper proposes an evolutionary optimization algorithm that employs a surrogate model as a fitness function to optimize methane/hydrogen SI engine performance and emissions. To create the surrogate model, we propose a novel ensemble learning algorithm that consists of several base learners. This paper employs ten different learning algorithms diversified via the Wagging method to create a pool of base-learner algorithms. This paper proposes a combinatorial evolutionary pruning algorithm to select an optimal subset of learning algorithms from a pool of base learners for the final ensemble algorithm. Once the base learners are designed, they are incorporated into an ensemble, where their outputs are aggregated using a weighted voting scheme. The weights of these base learners are optimized through a gradient descent algorithm. However, when optimizing a problem using surrogate models, the fitness function is subject to approximation uncertainty. To address this issue, this paper introduces an uncertainty reduction algorithm that performs averaging within a sphere around each solution. Experiments are performed to compare the proposed ensemble learning algorithm to the classical learning algorithms and state-of-the-art ensemble algorithms. Also, the proposed smoothing algorithm is compared with the state-of-the-art evolutionary algorithms. Experimental studies suggest that the proposed algorithms outperform the existing algorithms.

Papers:

M. Tayarani-N., A. Paykani, "An ensemble learning algorithm for optimization of spark ignition engine performance fuelled with methane/hydrogen blends", 2024, Applied Soft Computing, 112468

Date: 2024/11/22
Time: 14:00
Location: SP4024A & online

A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding

2024-01-22T14:06:02+00:00

On this week's Journal Club session, Mohammad Tayaraninajaran will talk about his paper "A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding".

The spread of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) which causes CoronaVirus Disease 2019 (COVID-19) has challenged many countries. To curb the effect of the pandemic requires the development of low-cost and rapid tools for detecting and diagnosing the patients. In this regard, chest X-ray scan images provide a reliable way of detecting the patients. One limitation, however, is the need for experts to analyse the images and identify the cases which can be a burden, when a large number of images are to be processed. The aim of this paper is to propose a method to extract rapidly, from the X-ray images, the regions in which there exist indications of COVID-19 infection. To identify the regions, image segmentation is required which is performed in this paper with a novel optimization algorithm. The proposed optimization algorithm uses probabilistic representation for the solutions. To improve the optimization process, we propose a diversity preserving operator. For multi-level image thresholding via optimization algorithms, different fitness functions have been proposed in the literature. In the proposed method in this paper, we use three fitness functions to benefit from the advantages of all. A fitness swapping scheme is proposed which swaps between the fitness functions in the optimization process. Also, a diversity preserving operator is proposed in this paper which compares the individuals and reinitializes the similar ones to inject diversity in the population. The proposed algorithm is tested on a number of COVID-19 benchmark images and experimental analysis suggest better performance for the proposed algorithm.

Papers:

M. Najaran, "A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding", 2023, Genetic Programming and Evolvable Machines, 24, 14

Date: 2024/01/26
Time: 14:00
Location: C258 & online

UH Biocomputation Group - Mohammad Tayaraninajaran

An ensemble learning algorithm for optimization of spark ignition engine performance fuelled with methane/hydrogen blends

A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding