Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

This work proposes the use of a specialized algorithm based on evolutionary computation to the global MPPT regulation of panel of thermoelectric modules connected serially in numerous string sections. Each section of the thermovoltaic panel is equipped with local DC/DC converter controlled by the proposed algorithm and finally this allows the optimization of the total efficiency of conversion. Evolutionary computations adjust PWM signals of switching waveforms of DC/DC sectional simple boost converters, which have outputs configured in parallel. It gives the chance to obtain the highest level of electric energy harvested, i.e., thanks to boost converting operational points precise adaptation to the system temperature profile as well as electric load level. The simulation results of the proposed evolutionary technique confirmed the high speed of the MPPT process that is much better than for perturbation and observation, as well as incremental conductance methods, and it assures concurrent optimization of numerous PWM signals. Next, the work shows practical optimization results achieved by the proposed algorithm implemented to microcontroller module controlling the DC/DC converter during thermal to electric conversion experiment. A laboratory thermovoltaic panel was constructed from a string of Peltier modules and radiator that assured passive cooling. The measurements obtained once more proved the MPPT evolutionary regulation properness and its adaptation effectiveness for different resistive test loads.

A Survey on Multi Objective Optimization Challenges in Swarm Intelligence

Various challenges in real life are multi-objective and conflicting (i.e., alter concurrent optimization). This implies that a single objective is optimized based on another’s cost. The Multi-Objective Optimization (MOO) issues are challenging but potentially realistic, and due to their wide-range application, optimization challenges have widely been analyzed by research with distinct scholarly bases. Resultantly, this has yielded distinct approaches for mitigating these challenges. There is a wide-range literature concerning the approaches used to handle MOO challenges. It is important to keep in mind that each technique has its pros and limitations, and there is no optimum alternative for cure searchers in a typical scenario. The MOO challenges can be identified in various segments e.g., path optimization, airplane design, automobile design and finance, among others. This contribution presents a survey of prevailing MOO challenges and swarm intelligence approaches to mitigate these challenges. The main purpose of this contribution is to present a basis of understanding on MOO challenges.

Predicting temperature-dependent ultimate strengths of body-centered-cubic (BCC) high-entropy alloys

This paper presents a bilinear log model, for predicting temperature-dependent ultimate strength of high-entropy alloys (HEAs) based on 21 HEA compositions. We consider the break temperature, Tbreak, introduced in the model, an important parameter for design of materials with attractive high-temperature properties, one warranting inclusion in alloy specifications. For reliable operation, the operating temperature of alloys may need to stay below Tbreak. We introduce a technique of global optimization, one enabling concurrent optimization of model parameters over low-temperature and high-temperature regimes. Furthermore, we suggest a general framework for joint optimization of alloy properties, capable of accounting for physics-based dependencies, and show how a special case can be formulated to address the identification of HEAs offering attractive ultimate strength. We advocate for the selection of an optimization technique suitable for the problem at hand and the data available, and for properly accounting for the underlying sources of variations.

A sensitivity interpolation algorithm for the concurrent optimization of bodies sharing a common design space

In this paper the problem of the concurrent topological optimization of two different bodies sharing a region of the design space is dealt with. This design problem focuses on the simultaneous optimization of two bodies (components) where not only the material distribution of each body has to be optimized but also the design space has to be divided among the two bodies. This novel optimization formulation represents a design problem in which more than one component have to be located inside a limited allowable room. Each component has its own function and load carrying requirements. In the paper a novel development solution algorithm is presented. With respect to previously published papers, the new algorithm comprises an interpolation of the density fields which allows a complete independence of the meshes of the two bodies. As the bodies can be meshed with any arbitrary mesh, this new algorithm can be applied to any real geometry. The developed algorithm is used to design a complex three dimensional system, namely a multi-component arm for a tube bending machine.

Towards the Concurrent Optimization of the Server: A Case Study on Sport Health Simulation

Using computers to conduct human body simulation experiments (e.g., human sport simulation, human physiology simulation, and human clothing simulation) can benefit from both economic and security. However, the human simulation experiment usually requires vast computational resources due to the complex simulation model which combines complicated mathematical and physical principles. As a result, the simulation process is usually time-consuming and simulation efficiency is low. One solution to address the issue of simulation efficiency is to improve the computing performance of the server when the complexity of the simulation model is determined. In this paper, we proposed a concurrent optimization scheme for the server that runs simulation experiments. Specifically, we firstly propose the architecture of the server cluster for the human body simulation, and then we design the concurrent optimization scheme for the server cluster by using Nginx. The experiment results show that the proposed concurrent optimization scheme can make better use of server resources and improve the simulation efficiency in the case of human sport simulation.

Question Selection in Template-Based Test Paper Models

The success of any educational program depends on its evaluation system. Examinations are a part of learning process which acts as an element in evaluation. For the smooth conduct of examinations of various universities and academic institutions, the test paper generation process would be helpful. However, examination test paper composition is a multi-constraint concurrent optimization problem. Question selection plays a key role in test paper generation systems. Also, it is the most significant and time-consuming activity. The question selection is handled in traditional test paper generation systems by using a specified test paper format containing a listing of weightages to be allotted to each unit/module of the syllabus.