Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II

In this study, an irreversible Diesel cycle is investigated based on entransy method and optimization conducted by considering various scenarios. Entransy expressed as a heat transfer potential of a subject and it has begun to investigate as a new thermodynamic assessment parameter. This paper presents thermodynamical study of an irreversible Diesel cycle in order to optimize its performance. Moreover, four different strategies in the process of multiobjective optimization are proposed, and the outcomes are assessing separately. The first strategy is proposed to minimize the entransy dissipation rate and maximize the ecological coefficient of performance along with the maximum available work. Furthermore, the second one is suggested to minimize the entransy dissipation rate and maximize the ecological function along with the exergetic performance criteria. The third strategy is proposed to minimize ethe entransy dissipation rate and maximize the ecological function along with the thermal efficiency. Moreover, the fourth strategy is suggested to achieve the minimum entransy dissipation rate and maximum ecological function along with the entransy efficiency. Finally, the values of objective functions were obtained at the optimum conditions of each scenario; afterwards, compared with other scenarios to select the most appropriate one. Based on obtained results, maximizing ecological coefficient of performance is the most appropriate approach for optimization, since it leads to the maximum values of ecological function, exergetic performance criteria and thermal efficiency. In addition, in the fourth scenario the lowest entransy dissipation rate was achieved. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 1475–1490, 2018

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“…More explanations about genetic algorithm can be found in literatures . The general concept of genetic algorithms is depicted in Figure .…”

Section: System Description and Optimization Algorithmmentioning

confidence: 99%

New thermodynamic analysis and optimization of performance of an irreversible diesel cycle

Ahmadi

Açıkkalp

et al. 2017

Env Prog and Sustain Energy

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“…The procedural steps of this algorithm are those described by R. Arora, Kaushik, and R. Arora (2015) and Deb et al (2002).…”

Section: Solution Methodologymentioning

confidence: 99%

A Credibilistic Mean-Semivariance-Per Portfolio Selection Model for Latin America

García

González-Bueno

Oliver

et al. 2019

Journal of Business Economics and Management

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Many real-world problems in the financial sector have to consider different objectives which are conflicting, for example portfolio selection. Markowitz proposed an approach to determine the optimal composition of a portfolio analysing the trade-off between return and risk. Nevertheless, this approach has been criticized for unrealistic assumptions and several changes have been proposed to incorporate investors’ constraints and more realistic risk measures. In this line of research, our proposal extends the mean-semivariance portfolio selection model to a multiobjective credibilistic model that besides risk and return, also considers the price-to-earnings ratio to measure portfolio performance. Uncertain future returns and PER ratio of each asset are approximated using L-R power fuzzy numbers. Furthermore, we consider budget, bound and cardinality constraints. To solve the constrained portfolio optimization problem, we use the algorithm NSGA-II. We assess the proposed approach generating a portfolio with shares included in the Latin American Integrated Market. Results show that this new approach is a good alternative to solve the portfolio selection problem when multiple objectives are considered.

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“…In this paper, two heuristic optimization techniques are used for calibration to find optimal PSS parameters. First, the NSGA II multiobjective optimization algorithm, for several-possibly conflicting-objective functions, [33][34][35][36][37]; and second, the Tabu search algorithm, for a single associated objective. Table 1.…”

Section: Methodology For Heuristic-stochastic Approachmentioning

confidence: 99%

Tuning of Controllers in Power Systems Using a Heuristic-Stochastic Approach

et al. 2019

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A method is proposed to fit parameters of Power System Stabilizer controllers in electromechanical multimachine power systems. The use of the Non-dominated Sorting Genetic Algorithm II heuristic method and Tabu search is considered to be initial search criteria. These methods give an approximation of the values that define the controllers. Then, the stochastic approach was used to evaluate the behavior of the parameters found when considering the system’s response to the presence of random and self-sustained in-time disturbances that affect the response of the system under steady state. The stochastic approach allows the evaluation of the system’s response through the calculation of the cost of energy loss under steady state. The method is applied to two systems: a three-machine nine-busbar system, and the Interconnected System of the Greater North (Sistema Interconectado del Norte Grande) in Chile. For these systems, the proposed methodology effectively optimized the controllers and Tabu search was shown to have a better performance than the Non-dominated Sorting Genetic Algorithm II.

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Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II

Cited by 76 publications

References 29 publications

New thermodynamic analysis and optimization of performance of an irreversible diesel cycle

New thermodynamic analysis and optimization of performance of an irreversible diesel cycle

A Credibilistic Mean-Semivariance-Per Portfolio Selection Model for Latin America

Tuning of Controllers in Power Systems Using a Heuristic-Stochastic Approach

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