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作者： Neto, Júlio Xavier Vianna De Andrade Bernert, Diego Luis Dos Santos Coelho, Leandro Pontifical Catholic University of Parana PUCPR Undergraduate Program at Mechatronics Engineering Imaculada Conceição 1155 Zip code 80215-901 Curitiba Parana Brazil Pontifical Catholic University of Parana PUCPR Industrial and Systems Engineering Graduate Program LAS/PPGEPS Imaculada Conceição 1155 80215-901 Curitiba Parana Brazil

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature. © 2010 Elsevier Ltd. All rights reserved.

关键词： Optimization

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A Harmony search algorithm combined with differential operator applied to reliability-redundancy optimization

A Harmony search algorithm combined with differential operat...

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2009 IEEE International Conference on systems, Man and Cybernetics, SMC 2009

作者： Dos Santos Coelho, Leandro De Andrade Bernert, Diego Luis Mariani, Viviana Cocco Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424427949

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. This paper introduces a modified HS approach combined with an operator of differential evolution - a paradigm of evolutionary computation - to solve optimization problems in reliability engineering. In this context, an example of mixed integer programming in reliability-redundancy design of an over-speed protection system for a gas turbine is evaluated. In this application domain, HS was found to outperform the previously best-known solutions available. ©2009 IEEE.

关键词： Integer programming

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Controller tuning with multi-objective optimization: benchmark definitions for SISO linear systems: A PI controller case ⁎

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IFAC-PapersOnLine 2024年第7期58卷 126-131页

作者： Gilberto Reynoso-Meza Ricardo Massao Kagami Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR)

Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, despite the expressive volume of works dealing with it, there is no standard benchmark testbed that could be used as a baseline comparison among different techniques. This work addresses such a gap, with a first step proposing a linear-SISO testbed with guidelines and rules and providing a basic example for a PI controller. Such a benchmark is expected to promote further research on the topic.

关键词： Multi-objective optimization Parametric optimization Evolutionary Algorithms

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A normative differential evolution approach for estimation of heat transfer coefficient during freezing treatment by inverse analysis

A normative differential evolution approach for estimation o...

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IEEE Congress on Evolutionary Computation

作者： Mariani, Viviana Cocco Klein, Carlos Eduardo Luvizotto, Luiz Guilherme Justi Dos Santos Coelho, Leandro Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paran Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424478347

Among the existing meta-heuristic optimization algorithms, a well-known branch is the differential evolution (DE). DE is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems which only has a few control parameters. With an eye to improve the performance of DE, in this paper, a DE approach combined with a cultural algorithm technique based on normative knowledge (NDE) is investigated to estimate the heat transfer coefficient during freezing treatment by inverse analysis. Numerical results for inverse heat transfer problem demonstrate the applicability and efficiency of the NDE algorithm. In this application, NDE approach outperforms a classical DE approach in terms of quality of solution. © 2011 IEEE.

关键词： Inverse problems

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Cauchy particle swarm optimization with dynamic adaptation applied to inverse heat transfer problem

Cauchy particle swarm optimization with dynamic adaptation a...

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2010 IEEE International Conference on systems, Man and Cybernetics, SMC 2010

作者： Mariani, Viviana Cocco Neckel, Vagner Jorge Grebogi, Rafael Bartnik Dos Santos Coelho, Leandro Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424465880

The particle swarm optimization (PSO) algorithm is a member of the wide category of swarm intelligence methods for solving global optimization problems. Its basic idea is the simulation of simplified animal social behaviors such as fish schooling and bird flocking. PSO algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. In this paper, a modified version of the original PSO based on Cauchy distribution and dynamic adaptation of inertia factor, named modified PSO (MPSO), is proposed. to estimate the unknown variables of an inverse heat transfer problem. To validate the optimization performance of the proposed MPSO, an inverse heat transfer problem is illustrated and the algorithm has to estimate its unknown variables. The results testify that the MPSO can perform well in an inverse heat transfer problem. ©2010 IEEE.

关键词： Heat transfer

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A chaotic firefly algorithm applied to reliability-redundancy optimization

A chaotic firefly algorithm applied to reliability-redundanc...

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Congress on Evolutionary Computation

作者： Leandro dos Santos Coelho Diego Luís de Andrade Bernert Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Mechanical Engineering PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer non linear programming. On the other hand, a broad class of meta heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available.

关键词： Fires Optimization Chaos Algorithm design and analysis Benchmark testing Redundancy

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Population's variance-based Adaptive Differential Evolution for real parameter optimization

Population's variance-based Adaptive Differential Evolution ...

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Congress on Evolutionary Computation

作者： Leandro dos Santos Coelho Helon V. H. Ayala Roberto Zanetti Freire Industrial and Systems Engineering Pontifical Catholic University of Parana Curitiba Parana Brazil Graduate Program-PPGEPS Department of Electrical Engineering-DEE PPGEE Federal University of Parana Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program-PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil

ISBN: (纸本)9781479904532

Differential evolution (DE) is an evolutionary algorithm (EA) that uses a rather greedy and less stochastic approach to solve optimization problems than other evolutionary methods [1]. Like other EAs, DE is a population-based, stochastic global optimizer, capable of working reliably in nonlinear and multimodal environments. Due to several features such as simplicity, efficiency and global search capabilities, DE rapidly became a successful paradigm of evolutionary computation. However, to achieve adequate performance with DE, the process of tuning the control parameters is essential as its performance is sensitive to the choice of both mutation and crossover settings. This paper proposes a DE algorithm with adaptive tuning of scaling factor (F), crossover rate (CR) and quasi-oppositional probability based on population's variance information - Adaptive Differential Evolution (ADE). Furthermore, ADE adopts a vector called Fm in each dimension of the optimization problem instead of single variable for F as presented in the classical DE approach. The proposed optimization method is validated on the test-bed proposed for the IEEE CEC'13 (IEEE Congress on Evolutionary Computation 2013) contest for real parameter single objective optimization with 28 benchmark functions. Simulation results over the benchmark functions demonstrate the effectiveness and usefulness of the proposed ADE method. This version of paper includes the ADE's performance on the 10, 30 and 50-dimensional benchmark functions.

关键词： Optimization Sociology Statistics Vectors Benchmark testing Evolutionary computation Frequency modulation

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A chaotic firefly algorithm applied to reliability-redundancy optimization

A chaotic firefly algorithm applied to reliability-redundanc...

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IEEE Congress on Evolutionary Computation

作者： Dos Santos Coelho, Leandro De Andrade Bernert, Diego Luis Mariani, Viviana Cocco Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paran Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paran Imaculada Conceição 1155 80215-901 Curitiba Paran Brazil

ISBN: (纸本)9781424478347

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available. © 2011 IEEE.

关键词： Integer programming

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Space and Time Efficiency Analysis of Data-Driven Methods Applied to Embedded systems

Space and Time Efficiency Analysis of Data-Driven Methods Ap...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Iron Tessaro Roberto Zanetti Freire Viviana Cocco Mariani Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

ISBN: (纸本)9781728190495

One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises over time, the energy efficiency of such systems must be considered in the design. The time (processor) efficiency of the embedded software is directly related to the energy efficiency of the embedded system. Therefore, when considering some embedded software solutions, such as data-driven methods, time efficiency must be taken into account to improve energy efficiency. In this work, the energy efficiency of three data-driven methods: the Sparse Identification of Nonlinear Dynamics (SINDy), the Extreme Learning Machine (ELM), and the Random-Vector Functional Link (RVFL) network were assessed by using the creation of a real-time in-cylinder pressure sensor for diesel engines as a task. The three methods were kept with equivalent performances, whereas their relative execution time was tested and classified by their statistical rankings. Additionally, the space (memory) efficiency of the methods was assessed. The contribution of this work is to provide a guide to choose the best data-driven method to be used in an embedded system in terms of efficiency.

关键词： Support vector machines Ranking (statistics) Radio frequency Embedded systems Neurons Energy efficiency Real-time systems

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Going in Circles: Key Aspects for Circular Economy Contributions to Agro-industrial Cooperatives

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Circular Economy and Sustainability 2023年第2期3卷 861-880页

作者： Barros, Murillo Vetroni de Jesus, Rômulo Henrique Gomes Ribeiro, Bruno Silva Piekarski, Cassiano Moro Sustainable Production Systems Laboratory (LESP) Graduate Program in Industrial Engineering (PPGEP) Universidade Tecnológica Federal Do Paraná (UTFPR) Paraná Ponta Grossa Brazil Department of Industrial Engineering Universidade Estadual do Paraná (UNESPAR) Paraná Paranaguá Brazil Sustainable Production Systems Laboratory (LESP) Universidade Tecnológica Federal Do Paraná (UTFPR) Paraná Ponta Grossa Brazil

The global agribusiness context faces at the same time challenges of feeding a growing global population that is used to safe and nutritious food, opportunities based on innovation, high technology and efficiency in the agri-food production systems from field to table. Given the context, this article aims to present the main key aspects of the circular economy to agro-industrial cooperatives. These contributions were named key aspects, such as circular and symbiotic practices, competitive advantages, innovation, cooperation and barriers and opportunities. The field presents a problem that few literature in fact discusses circular economy approaches in the context of agro-industrial cooperatives. As a methodological procedure, a literature review was carried out in three databases to obtain relevant documents and thus analyse and discuss some characteristics based on circular economy practices applied in agro-industrial cooperatives. As a result, a diagnosis of the current scenario that agro sector organizations are facing in terms of the main rural activities and sustainable practices that relate to the circular economy;measures taken to generate competitive advantage;innovation behaviour;and how linear and circular business models are being applied in the agroindustry. A framework is presented to show potential routes strategies for closing the cycle in an agro-industrial cooperative. The opportunities are based on the implementation of high technology in the field, the use of bioenergy and the development of new circular business models throughout the agro-value chain. The study has contributions to rural properties and managers of cooperatives in terms of waste reduction, innovation generation and increases in activities and processes based on circular economy. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Agro-industrial business Circular economy Circularity Cooperation Farm

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