Some Experiments on Fitting of Gielis Curves by Simulated Annealing and Particle Swarm Methods of Global Optimization

In this paper an attempt has been made to fit the Gielis curves (modified by various functions) to simulated data. The estimation has been done by two methods - the Classical Simulated Annealing (CSA) and the Particle Swarm (PS) methods - of global optimization. The Repulsive Particle Swarm (RPS) optimization algorithm has been used. It has been found that both methods are quite successful in fitting the modified Gielis curves to the data. However, the lack of uniqueness of Gielis parameters to data (from which they are estimated) is corroborated. From a technical viewpoint, this exercise may be considered as an application of CSA and RPS to extremely nonlinear least-squares curve-fitting to data that may exhibit a large number of local optima.Gielis curves; superformula; nonlinear curve-fitting; Least squares; multi-modal; local optima; global optimization; simulated annealing; particle swarm; parameters estimation

Analysis of Particle Swarm-Aided Power Plant Optimization

Stochastic optimization algorithms are usually evaluated based on performance on high dimensional benchmark functions and results of these tests determine the direction of development. Benchmark functions however, do not emulate complex engineering problems. In this paper a power plant optimization problem is presented and solved under different constraints with multiple elite dependent and single elite dependent swarm intelligence. Although on benchmark problems multiple elite dependent algorithms usually outperform single elite dependent ones, if search space is represented by simulation software, diversity not just increases iterations but computation time as well and because of that conventional PSO (particle swarm optimization) exceeds modified ones

Particle Swarm algorithm with Fuzzy decision making for a multi-objective economic and environmental optimization of design of a thermal system

Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced

Optimized powerplant configurations for improved rotorcraft operational performance

This paper presents an integrated multidisciplinary rotorcraft design and optimization framework, deployed for the design and assessment of a conceptual rotorcraft powerplant configuration at mission level. The proposed approach comprises a wide-range of individual modeling theories applicable to rotorcraft flight dynamics, gas turbine engine performance and weight estimation as well as a novel physics-based, stirred reactor model for the rapid estimation of gas turbine gaseous emissions. A novel Single-Objective and Multi-Objective Particle Swarm Optimizer is coupled with the aforementioned integrated rotorcraft multidisciplinary design framework. The combined approach is applied to the multidisciplinary design and optimization of a reference Twin Engine Light civil rotorcraft modeled after the Eurocopter Bo105 helicopter, operating on representative mission scenario. Through the application of Single-Objective optimization, optimum engine design configurations are acquired in terms of mission fuel consumption, engine weight and gaseous emissions at constant technology level. Multi-Objective studies are carried out in order to quantify the optimum interrelationship between mission fuel consumption and gaseous emissions for the representative Twin Engine Light rotorcraft operation and a variety of engine configurations. The proposed approach essentially constitutes an enabler in terms of focusing the multidisciplinary design of rotorcraft powerplants to realistic, three-dimensional operations and towards the realization of associated engine design tradeoffs at mission level

Exergoeconomic optimization of a thermal power plant using particle swarm optimization

The basic concept in applying numerical optimization methods for power plants optimization problems is to combine a State of the art search algorithm with a powerful, power plant simulation program to optimize the energy conversion system from both economic and thermodynamic viewpoints. Improving the energy conversion system by optimizing the design and operation and studying interactions among plant components requires the investigation of a large number of possible design and operational alternatives. State of the art search algorithms can assist in the development of cost-effective power plant concepts. The aim of this paper is to present how nature-inspired swarm intelligence (especially PSO) can be applied in the field of power plant optimization and how to find solutions for the problems arising and also to apply exergoeconomic optimization technics for thermal power plants

Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8

Modeling of a Modern Aircraft Through Calibration Techniques

NASA is seeking a new baseline aircraft model to assess the state-of-the-art technology for aircraft noise, emissions, and fuel/energy consumption as an update to a 2005 baseline. The process of modeling engine and airframe models as a system has historically required many iterations at NASA between the airframe and engine models. A new internal process presented in this paper contains a method that simultaneously calibrates an airframe and engine model to known data to create an aircraft system model. The work presented in this paper proposes a new framework in creating new aircraft models for future NASA research. This approach is presented as a general outline applicable to any chosen commercial aircraft. As an applied example, the B737 MAX 8 aircraft is chosen as the integrated engine and airframe model subjected to calibration. Initial results show a close match to available data but further refinement in the process is necessary for this ongoing work