Dan Simon - Academia.edu (original) (raw)
Papers by Dan Simon
2021 IEEE Conference on Control Technology and Applications (CCTA)
2021 IEEE International Conference on Big Data and Smart Computing (BigComp)
2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2013
Current State and Future Perspectives
This research involves the development of an engineering test for a newly-developed evolutionary ... more This research involves the development of an engineering test for a newly-developed evolutionary algorithm called biogeography-based optimization (BBO), and also involves the development of a distributed implementation of BBO. The BBO algorithm is based on mathematical models of biogeography, which describe the migration of species between habitats. BBO is the adaptation of the theory of biogeography for the purpose of solving general optimization problems. In this research, BBO is used to tune a proportional-derivative control system for real-world mobile robots. The authors show that BBO can successfully tune the control algorithm of the robots, reducing their tracking error cost function by 65% from nominal values. This chapter focuses on describing the hardware, software, and the results that have been obtained by various implementations of BBO.
NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 2006
Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010
EURASIP Journal on Advances in Signal Processing, 2013
Engineering Applications of Artificial Intelligence, 2015
This paper analyzes the role of information and communication technology (ICT) and computer model... more This paper analyzes the role of information and communication technology (ICT) and computer modelling in the education of engineering students. Special attention is paid to research-based education and the implementation of new modelling methods and advanced software in student research, including course work, diploma projects, and theses for all student categories, including Doctoral, Master’s, and Bachelor’s. The paper concentrates on the correlation between student research and government priorities and research funding. Successful cases of such correlations with specific description of computer modeling methods for the implementation of prosthesis and robotics research projects are presented based on experiences in the Embedded Control Systems Research Laboratory in the Electrical Engineering and Computer Science Department in the Washkewicz College of Engineering, Cleveland State University, USA.
International Journal of Electrical Power & Energy Systems, 2018
One control challenge in prosthetic legs is seamless transition from one gait mode to another. Us... more One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six...
Engineering Applications of Artificial Intelligence, 2015
Engineering Applications of Artificial Intelligence, 2017
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017
Mathematical Problems in Engineering, 2014
Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, whic... more Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. A finite Markov chain model of BBO for binary problems was derived in earlier work, and some significant theoretical results were obtained. This paper analyzes the convergence properties of BBO on binary problems based on the previously derived BBO Markov chain model. Analysis reveals that BBO with only migration and mutation never converges to the global optimum. However, BBO with elitism, which maintains the best candidate in the population from one generation to the next, converges to the global optimum. In spite of previously published differences between genetic algorithms (GAs) and BBO, this paper shows that the convergence properties of BBO are similar to those of the canonical GA. In addition, the convergence rate estimate of BBO with elitism is obtained in this paper and is confirmed by simulations for some simple repr...
Computers & Operations Research, 2015
Evolutionary computation, Jan 14, 2015
Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, whic... more Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of ones in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those at the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamic...
2021 IEEE Conference on Control Technology and Applications (CCTA)
2021 IEEE International Conference on Big Data and Smart Computing (BigComp)
2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2013
Current State and Future Perspectives
This research involves the development of an engineering test for a newly-developed evolutionary ... more This research involves the development of an engineering test for a newly-developed evolutionary algorithm called biogeography-based optimization (BBO), and also involves the development of a distributed implementation of BBO. The BBO algorithm is based on mathematical models of biogeography, which describe the migration of species between habitats. BBO is the adaptation of the theory of biogeography for the purpose of solving general optimization problems. In this research, BBO is used to tune a proportional-derivative control system for real-world mobile robots. The authors show that BBO can successfully tune the control algorithm of the robots, reducing their tracking error cost function by 65% from nominal values. This chapter focuses on describing the hardware, software, and the results that have been obtained by various implementations of BBO.
NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 2006
Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010
EURASIP Journal on Advances in Signal Processing, 2013
Engineering Applications of Artificial Intelligence, 2015
This paper analyzes the role of information and communication technology (ICT) and computer model... more This paper analyzes the role of information and communication technology (ICT) and computer modelling in the education of engineering students. Special attention is paid to research-based education and the implementation of new modelling methods and advanced software in student research, including course work, diploma projects, and theses for all student categories, including Doctoral, Master’s, and Bachelor’s. The paper concentrates on the correlation between student research and government priorities and research funding. Successful cases of such correlations with specific description of computer modeling methods for the implementation of prosthesis and robotics research projects are presented based on experiences in the Embedded Control Systems Research Laboratory in the Electrical Engineering and Computer Science Department in the Washkewicz College of Engineering, Cleveland State University, USA.
International Journal of Electrical Power & Energy Systems, 2018
One control challenge in prosthetic legs is seamless transition from one gait mode to another. Us... more One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six...
Engineering Applications of Artificial Intelligence, 2015
Engineering Applications of Artificial Intelligence, 2017
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017
Mathematical Problems in Engineering, 2014
Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, whic... more Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. A finite Markov chain model of BBO for binary problems was derived in earlier work, and some significant theoretical results were obtained. This paper analyzes the convergence properties of BBO on binary problems based on the previously derived BBO Markov chain model. Analysis reveals that BBO with only migration and mutation never converges to the global optimum. However, BBO with elitism, which maintains the best candidate in the population from one generation to the next, converges to the global optimum. In spite of previously published differences between genetic algorithms (GAs) and BBO, this paper shows that the convergence properties of BBO are similar to those of the canonical GA. In addition, the convergence rate estimate of BBO with elitism is obtained in this paper and is confirmed by simulations for some simple repr...
Computers & Operations Research, 2015
Evolutionary computation, Jan 14, 2015
Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, whic... more Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of ones in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those at the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamic...