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Heuristic Kalman Algorithm for Multiobjective Optimization.

IFAC-PapersOnLine 2017年第1期50卷 4460-4465页

作者： Hultmann Ayala H.V. dos Santos Coelho L. Reynoso-Meza G. Industrial and Systems Engineering Graduate Program PPGEPS Polytechnic School Pontifical Catholic University of Paraná PUCPR Rua Imaculada Conceição 1155 Curitiba PR code 80215-901 Brazil Department of Electrical Engineering (PPGEE) Federal University of Parana (UFPR) Polytechnic Center Curitiba Parana 81531-970 Brazil

This papers presents a new heuristic multiobjective optimization procedure based on Kalman filtering. The novel approach is compared to the well-known Nondominated Sorting Genetic Algorithm version II with the Zitzler, Deb and Thiele test suite showing favorable results for the Kalman based solution. Further improvement on the proposed algorithm is made with respect to the learning factor, which is set to be time-dependent, improving the algorithm in most cases and diminishing the number of control parameters. The proposed multiobjective optimization algorithm is interesting due to its convergence and coverage quality, its computational complexity and few parameters to be adjusted. © 2017

关键词： loop pairing Multi-input/multi-output systems Multi-objective optimization Multiloop control

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Towards a smart reconfiguration process for complex product manufacturing based on industrial robot cells

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IFAC-PapersOnLine 2022年第2期55卷 108-113页

作者： Murillo Skrzek Leandro L. da Silva Anderson L. Szejka Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

The cost for companies that can custom their production line with precision and quality, through machining centres and laser cutting machines, is expensive, as the degree of machining freedom increases. Furthermore, the greater the complexity of the product to be manufactured, the more processes and equipment are needed to manufacture it. In this context, this research proposes a smart reconfiguration process for complex product manufacturing, based on industrial robotic manipulators, with 6 or more degrees of freedom, superior to conventional methods. With the integration of SolidWorks and PowerMill software, it was possible to apply the solution in an experimental case of blisk manufacturing milling and laser cutting of an engine head gasket. With CAD, CAM and robot simulation by the PowerMill tool, it was possible to validate the proposed solution, obtaining process data and information of milling and laser cutting. With the analysis of the simulation results, it was possible to identify the limitations of the project, such as (i) restriction in the precision of the machining of the product; (ii) amount of real-time supervision and monitoring information; and (iii) work area limit. However, the project can integrate two manufacturing processes to machine and cut complex parts with flexibility.

关键词： Robotic manipulator Milling Laser Cutting Reconfigurable Manufacturing systems Complex Product Computer-Aided Manufacturing

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Applications of multi-objective optimisation for PID-like controller tuning: a 2015-2019 review and analysis

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IFAC-PapersOnLine 2020年第2期53卷 7933-7940页

作者： Tainara Marques Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) CuritibaPR Brazil

The first open invited track in multi-objective optimisation for control systems was organised in 2017 with the idea of exchanging ideas and research about how those techniques are valuable for control engineers. Given that control engineering problems are generally multi-objective problems, multi-objective optimisation offers an interesting approach via the simultaneous optimisation of all design objectives. Controller tuning is not except from this. In this paper we perform a review and analysis of the literature, limited to the IFAC environment, to appreciate and detect new tendencies in controller tuning applications via multi-objective optimisation. Time window under consideration is from 2015 to date, coinciding with a previous review on the topic, as well as the emigration of IFAC proceedings to Elsevier.

关键词： Multi-objective optimisation Parametric optimisation Evolutionary Algorithms Intelligent Control Process Control

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Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack

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International Journal of Innovative Computing and Applications 2016年第4期7卷 214-224页

作者： de Almeida, Ricardo Steiner, Maria Teresinha Arns Industrial and Systems Engineering Post-Graduate Program Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

The objective of this work is to compare the augmented neural network (AugNN) metaheuristic to minimum bin slack (MBS) heuristic to solve combinatorial optimisation problems, specifically, in this case, the one-dimensional bin packing problem (1D-BPP), a class of cutting and packing problems (CPP). CPP are easily found among various industry sectors and its proper treatment can improve the use of stocks in cutting problems or optimise physical space in packing problems. In order to optimise AugNN parameters, a design of experiment (DOE) was applied in order to guide a statistical analysis of different configurations of AugNN. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of the solution quality, which is about 70% better, and computational time, which is about 90% less. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Design of experiments

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A Zaslavskii Firefly Approach Applied to Loney's Solenoid Benchmark

A Zaslavskii Firefly Approach Applied to Loney's Solenoid Be...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Leandro dos Santos Coelho Emerson Hochsteiner de Vasconcelos Segundo Viviana Cocco Mariani Marcia de Fatima Morais Roberto Zanetti Freire Industrial and Systems Eng. Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Department of Electrical Engineering Federal University of Parana (UFPR) Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Department of Electrical Engineering Federal University of Parana (UFPR) Industrial and Systems Eng. Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR)

ISBN: (纸本)9781479938414

Nature-inspired algorithms of the swarm intelligence field perform powerfully and efficiently in solving global optimization problems. Inspired by nature, these metaheuristic algorithms have obtained promising performance over continuous domains of optimization problems. Recently, a new swarm intelligence approach called firefly algorithm (FA) has emerged. The FA is a stochastic paradigm based on the idealized behavior of the flashing characteristics of fireflies. However, to achieve good performance with FA, the tuning of control parameters is essential as its performance is sensitive to the choice of the randomization parameter (α) setting. This paper introduces a FA approach combined with chaotic sequences generated by Zaslavskii map (FACZ) to tune the randomization parameter. Simulations of Loney's solenoid benchmark problem examine the effectiveness of the conventional FA and the proposed FACZ algorithms. Simulation results and comparisons with the FACZ demonstrated that the performance of the FA is promising in the Loney's solenoid case.

关键词： Swarm intelligence Firefly algorithm Electromagnetic optimization Chaotic sequences Loney's solenoid

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Exploring Bess Services for Transformer Energization on Offshore Oil and Gas Platforms

Exploring Bess Services for Transformer Energization on Offs...

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2024 IEEE Petroleum and Chemical Industry Conference Brasil, PCIC Brasil 2024

作者： Banda, Joseph K. Araujo, Lucas S. Brandao, Danilo I. Marafao, Fernando P. Tedeschi, Elisabetta Department of Electric Power Engineering Trondheim Norway Graduate Program in Electrical Engineering Belo Horizonte Brazil Department of Control and Automation Engineering Sorocaba Brazil University of Trento Department of Industrial Engineering Trento Italy

ISBN: (纸本)9798350368154

The electrification of offshore oil and gas platforms (OOGPs), using offshore wind turbines, is now a reality, aimed at minimizing carbon emissions. In this scenario, the utilization of battery energy storage systems (BESS) improves both efficiency and power quality. As this integration comes with a high CAPEX, industries demand exploring BESS for multiple ancillary services on OOGPs. Thus, this paper proposes an alternative approach to energizing a power transformer (PT) using the BESS power conversion system (PCS) on the platform, aiming to minimize inrush currents. This avoids the necessity for additional bulky devices, converters, or dc voltage sources specifically designated for this purpose. The strategy consists of manipulating residual core flux using BESS PCS followed by a controlled switching, which reduces inrush currents during PT energization. The results show effective reduction of the inrush current from 2.31 pu to 0.16 pu for the presented case study. © 2024 IEEE.

关键词： Power transformers

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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2007 IEEE International Conference on systems, Man, and Cybernetics, SMC 2007

作者： Dos Santos Coelho, Leandro Mariani, Viviana Coceo Industrial and Systems Engineering Graduate Program - PPGEPS Automation and Systems Laboratory Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Pontifical Catholic University of Paraná Mechanical Engineering Graduate Program PUCPR/PPGEM Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)1424409918

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an Implicit Filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. © 2007 IEEE.

关键词： Particle swarm optimization (PSO)

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Multiobjective Robust PI Synthesis in Plants with Uncertain Poles

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IFAC-PapersOnLine 2024年第7期58卷 174-179页

作者： Ricardo Massao Kagami Roberto Zanetti Freire Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Universidade Tecnológica Federal do Paraná (UTFPR) Brazil Control Systems Optimization Laboratory (LOSC) PUCPR Brazil

The use of multiobjective optimization procedures for control tuning has been constantly explored, presenting satisfactory results, and generating significant contributions to control engineering. In this way, the field of robust control can benefit significantly from the integration of multiobjective optimization design procedures for robust Proportional-Integral-Derivative (RPID) controller synthesis, enabling innovative solutions to complex control challenges. Due to this fact, this paper explores an approach to utilize multiobjective optimization algorithms in a simplified procedure, considering uncertain systems in the form of interval plants with uncertain poles, the Kharitonov theorem is used to obtain a region of stability as search space for the multiobjective algorithm, being the optimization process performed over a nominal system model. Two numerical cases and a flexible actuator model were used to demonstrate the procedure for RPI synthesis, resulting in robustly stable controllers with optimized performance over error and control signal effort measures.

关键词： Robust control multiobjective optimization robust stability Kharitonov theorem PI controller

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Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Improved quantum-inspired evolutionary algorithm with divers...

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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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Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies

Discrete Differential Evolution with Local Search to Solve t...

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IEEE International Conference on Cybernetic Intelligent systems

作者： Joao Guilherme Sauer Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PPGEPS/PUCPR)

ISBN: (纸本)9781424429141

Traveling Salesman Problem (TSP) is one of the most difficult problems in the Combinatorial Optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose in literature to solve TSP problems. Differential Evolution (DE) is a relatively new simple evolutionary algorithm, which is an effective adaptive approach to global optimization over continuous search spaces. The design principles of DE are simplicity and efficiency. The most distinct feature of DE is that it mutates vectors by adding weighted, random vector differentials to them. Since its invention, DE has been applied with high success on many numerical optimization problems outperforming other more popular meta-heuristics such as the genetic algorithms. Recently, some researchers extended with success the application of DE to complex combinatorial optimization problems with discrete decision variables such as the traveling salesman problem, the machine layout problem, the flow-shop scheduling problem. In this paper, the following discrete DE approaches for the TSP are proposed and evaluated: i) DE approach without local search, ii) DE with local search based on Lin-Kernighan-Heulsgaun (LKH) method, and iii) DE with local search based on Variable Neighborhood Search (VNS) and together with LKH method. Numerical study is carried out using the TSPLIB of test TSP problems. In this context, the computational results are compared with the other results in the recent TSP literature. The obtained results show that LKH method is the best method to reach optimal results for TSPLIB benchmarks, but for largest problems, the ED+VNS improve the quality of obtained results.

关键词： Optimization Traveling salesman problem Evolutionary Algorithm Differential Evolution Variable Neighbor Search Local Search Lin-Kernighan-Heulsgaun travelling salesman problems evolutionary algorithm MT design theory continuous search

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