Particle Swarm Optimization Heuristics Research Articles

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

Thermodynamic Optimization of Three-Fluid Cross-Flow Heat Exchanger Using GA and PSO Heuristics

Optimization technique for a three-fluid cross-flow plate-fin heat exchanger with offset strip fins is developed, considering significant variables subjected to given constraints. The objective of optimization is focused on maximizing the hot fluid effectiveness and minimizing the number of entropy generation units in the three-fluid cross-flow plate-fin heat exchanger. Governing equations for a cross-cocurrent flow arrangement of a three-fluid heat exchanger are solved using finite element method for given boundary conditions. The obtained mean exit fluid temperatures from FEM are used to determine the rate of entropy generation and hot fluid effectiveness of the heat exchanger. Results obtained from the optimization of a reduced model of two-fluid cross-flow heat exchanger is compared with previously published results, thus serving as a validation of the optimization technique. Geometric parameters of the heat exchanger are varied to get the optimum results. The present investigation uses two different heuristics, namely Genetic algorithm (GA) and Particle Swarm Optimization (PSO) to find the optimum design values based on the objectives. Minimization of number of entropy generation units is treated to be a single objective function and optimum solutions are determined using both GA and PSO, both giving a function value of 0.0534786. Similarly, considering maximization of the hot fluid effectiveness, the optimum function value from both techniques is found to be 0.99997095. The results obtained from both the methods are compared and provides almost identical design values, with PSO taking lesser time for execution. Also, considering minimization of number of entropy generation units and maximization of hot fluid effectiveness of the heat exchanger as objective functions, multi-objective optimization is performed using GA alone and the multiple results obtained has been illustrated as a pareto-front.