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Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering
Published in Horia-Nicolai Teodorescu, Abraham Kandel, Lakhmi C. Jain, FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE, 2017
Horia-Nicolai L. Teodorescu, Abraham Kandel, Lakhmi C. Jain
Evolutionary computing is the name given to a collection of algorithms based on the evolution of a population toward an optimal solution of a certain problem. Three types of evolutionary computing techniques have been widely reported: genetic algorithms (GAs), Genetic Programming (GP), and Evolutionary Programming (EP). GAs were applied to the optimization of fuzzy systems and neuro-fuzzy systems in the early 1990s (for instance: Karr and Chunk: Applying genetics to fuzzy logic. AI Expert, vol. 6, nr. 3, pp. 38-43, 1991). GAs are widely used and reported in the literature (for instance: Jang, J.-S.R., Sun, C.T., and Mizutani, E.: Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997; Van Rooij A., Jain, L.C., Johnson, R.P.: Neural Network Training Using Genetic Algorithms, World Scientific Publ. Co., Singapore, 1996).
Evolutionary fuzzy systems
Published in Ali Zilouchian, Mo Jamshidi, Intelligent Control Systems Using Soft Computing Methodologies, 2001
R. Mohammad, T. Akbarzadeh, A.H. Meghdad
There are various approaches to evolutionary optimization algorithms including evolution strategies, evolutionary programming, genetic programming and genetic algorithms. These various algorithms are similar in their basic concepts of evolution and differ mainly in their approach to parameter representation [1]. Genetic algorithms (GA), in particular, is an evolutionary method which has performed well in noisy, nonlinear, and uncertain optimization landscapes typical of fuzzy systems. In this chapter, we will explore further why and how GA is used for optimization of fuzzy systems and, in particular, fuzzy controllers. Various issues such as determining the set of parameters, designing the transformation function for representing the parameter space in genetic domain, creating the initial population, and determining the fitness function will be discussed. Finally, application of GA will be illustrated in optimizing fuzzy control of a DC motor.
Evolutionary computation
Published in Richard E. Neapolitan, Xia Jiang, Artificial Intelligence, 2018
Richard E. Neapolitan, Xia Jiang
The remaining two areas of evolutionary computation are evolutionary programming and evolution strategies. Evolutionary programming is similar to genetic algorithms in that it uses a population of candidate solutions to evolve into an answer to a specific problem. Evolutionary programming differs in that the concentration is on developing behavioral models of the observable system interaction with the environment. Fogel [1994] presents this approach. Evolution strategies models problem solutions as species. Rechenberg [1994] says that the field of evolution strategies is based on the evolution of evolution. See [Kennedy and Eberhart, 2001] for a complete introduction to all four areas of evolutionary computation.
Optimal policy of hydroelectric reservoir integrated spill flow
Published in Journal of Applied Water Engineering and Research, 2023
Daniel Eutyche Mbadjoun Wapet, Salomé Ndjakomo Essiane, René Wamkeue, Dieudonné Bisso, Patrick Juvet Gnetchejo, Mohit Bajaj
The notion of evolution in biology focuses on the ability of individuals to adapt to environmental changes. The most suitable are retained through processes such as natural selection, reproduction, mutation, competition, symbiosis or immigration. Evolutionary algorithms use a population of individuals governed by the operators translating these mechanisms. The choice and the order in which these operators occur have given rise to several evolutionary algorithms. For example, combinations, mutations and selections have given rise to the genetic algorithm (GA), genetic programming (with a different representation of the individual in relation to the GA), evolutionary programming (simulating phenotypic adaptive behaviour), evolutionary strategies (simulating evolutionary rules or evolutionary evolution) and differential evolution. To this list, we can add the optimization based on biogeography, which simulates the geographical distribution of species through migration and emigration. Co-evolution funded on the evolution of two populations (i.e. two different objective functions) in a symbiotic (cooperation) or predator-prey (competition) relationship represents another class of evolutionary algorithm. In this paper, we have chosen to present differential evolution.
Optimal sizing of a grid-connected hybrid renewable energy systems considering hydroelectric storage
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Tong Wu, Hao Zhang, Lixia Shang
The meta-heuristic algorithms fall into two dominant categories: swarm and evolutionary intelligence algorithms (Erdinc and Uzunoglu 2012; Khan et al. 2019; Khenfous et al. 2018; Yang et al. 2018; Zahoor et al. 2018). Evolutionary algorithms imitative the conceptions of evolution in nature; there are many evolutionary algorithms in the literature such as Evolutionary Strategy (ES), Evolutionary Programming (EP), Differential Evolution (DE), Biogeography-Based Optimization (BBO), Genetic Algorithm (GA) (Lokeshgupta and Sivasubramani 2018), and Particle Swarm Optimization (Kumar et al. 2019). GA and PSO can adequately investigate the distinctive locale of search space at once. Subsequently, GA and PSO are less defenseless to being caught in local minimum when contrasted with a regular approach. The authors in (Kumar et al. 2019) have used PSO for sizing and optimization of hybrid hydro/PV/Diesel system in different regions in Western Himalayan. Cuckoo Search (CS) is a recently used meta-heuristic algorithm for solving the optimization problems of HRES (Mohamed et al. 2019). The main basis of the intelligence swarm algorithms stems from the additive manner of a set of creatures. In the Salp Swarm Algorithm (SSA), the parameters to be adjusted during the optimization process are less than those of GA and PSO. This, in turn, makes SSA more potential and superior among the optimization techniques in solving a wide class of optimization problems (Yang et al. 2019a).
Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm
Published in International Journal of Green Energy, 2018
Hamid HassanzadehFard, Alireza Jalilian
DG sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to the environmental friendliness and also the limited sources of fossil fuels. Because of the oil crisis in the early 1970s, the utilization of solar and wind powers have become increasingly significant, attractive, and cost-effective (Diaf et al. 2007). The selections of the best places for installation and the preferable size of the DG units in distribution systems are categorized as a complex combinatorial optimization problem. Recently, optimization tools have been employed to solve different DG-unit problems. For example, Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), Cuckoo Search (CS), etc. have been used successfully in this field and they are still evolving for more complex problems.