Determining optimal parameters for stereolithography processes via genetic algorithm

This paper presents a comparative study for three evolutionary algorithms (EAs) to the optimal reactive power planning (ORPP) problem: evolutionary programming, evolutionary strategy, and genetic algorithm. The ORPP problem is decomposed into P- and Q-optimization modules, and each module is optimized by the EAs in an iterative manner to obtain the global solution. The EA methods for the ORPP problem are evaluated against the IEEE 30-bus system as a common testbed, and the results are compared against each other and with those of linear programming.

An improved particle swarm optimization algorithm for unit commitment

Neural networks and fuzzy systems are two soft‐computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general‐purpose stochastic global optimization approach under the universally accepted neo‐Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers

Chapter 15 - When Evolutionary Computing Meets Astro- and Geoinformatics

Real-world has many optimization scenarios with multiple constraints and objective functions that are discontinuous, nonlinear, non-convex, and multi-modal in nature. Also, the optimization problems are multi-dimensional with mixed types of variables like integer, real, discrete, binary, and having a different range of values which demands normalization. Hence, the search space of the problem cannot be smooth. Evolutionary algorithms have started gaining attention and have been employed for computational processes to solve complex engineering problems. Because it has become an instrument for research scientists and engineers who need to apply the supremacy of the theory of evolution to shape any optimization-based research problems and articles. In this chapter, there is a comprehensive introduction to the optimization field with the state-of-the-art in evolutionary computation. Though many books have described such areas of optimization in any form as evolution strategies, genetic programming, genetic algorithms, and evolutionary programming, evolutionary algorithms, that is, evolutionary computation is remarkable for considering it to discuss in detail as a general class.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.

Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

Evolutionary computation (EC) is the study of computational systems that borrow ideas from and are inspired by natural evolution and adaptation (Yao & Xu, 2006, pp. 1-18). EC covers a number of techniques based on evolutionary processes and natural selection: evolutionary strategies, genetic algorithms and genetic programming (Keedwell & Narayanan, 2005). Evolutionary strategies are an approach for efficiently solving certain continuous problems, yielding good results for some parametric problems in real domains. Compared with genetic algorithms, evolutionary strategies run more exploratory searches and are a good option when applied to relatively unknown parametric problems. Genetic algorithms emulate the evolutionary process that takes place in nature. Individuals compete for survival by adapting as best they can to the environmental conditions. Crossovers between individuals, mutations and deaths are all part of this process of adaptation. By substituting the natural environment for the problem to be solved, we get a computationally cheap method that is capable of dealing with any problem, provided we know how to determine individuals’ fitness (Manrique, 2001). Genetic programming is an extension of genetic algorithms (Couchet, Manrique, Ríos & Rodríguez- Patón, 2006). Its aim is to build computer programs that are not expressly designed and programmed by a human being. It can be said to be an optimization technique whose search space is composed of all possible computer programs for solving a particular problem. Genetic programming’s key advantage over genetic algorithms is that it can handle individuals (computer programs) of different lengths. Grammar-guided genetic programming (GGGP) is an extension of traditional GP systems (Whigham, 1995, pp. 33-41). The difference lies in the fact that they employ context-free grammars (CFG) that generate all the possible solutions to a given problem as sentences, establishing this way the formal definition of the syntactic problem constraints, and use the derivation trees for each sentence to encode these solutions (Dounias, Tsakonas, Jantzen, Axer, Bjerregard & von Keyserlingk, D. 2002, pp. 494-500). The use of this type of syntactic formalisms helps to solve the so-called closure problem (Whigham, 1996). To achieve closure valid individuals (points that belong to the search space) should always be generated. As the generation of invalid individuals slows down convergence speed a great deal, solving this problem will very much improve the GP search capability. The basic operator directly affecting the closure problem is crossover: crossing two (or any) valid individuals should generate a valid offspring. Similarly, this is the operator that has the biggest impact on the process of convergence towards the optimum solution. Therefore, this article reviews the most important crossover operators employed in GP and GGGP, highlighting the weaknesses existing nowadays in this area of research. We also propose a GGGP system. This system incorporates the original idea of employing ambiguous CFG to overcome these weaknesses, thereby increasing convergence speed and reducing the likelihood of trapping in local optima. Comparative results are shown to empirically corroborate our claims.

In recent years population based methods such as genetic algorithms, evolutionary programming, evolution strategies and genetic programming have been increasingly employed to solve a variety of optimisation problems. Recently, another novel population based optimisation algorithm - namely the particle swarm optimisation (PSO) algorithm, was introduced by R. Eberhart and J. Kennedy (1995). Although the PSO algorithm possesses some attractive properties, its solution quality has been somewhat inferior to other evolutionary optimisation algorithms (P. Angeline, 1998). We propose a number of techniques to improve the standard PSO algorithm. Similar techniques have been employed in the context of self organising maps and neural-gas networks (T. Kohonen, 1990; T.M. Martinez et al., 1994).

The digitalization of data has recently had an impact on the building industry. Building information modeling (BIM) is a highly developed technology that is used to predict the cost, lifespan, energy use, and efficiency of buildings. To enhance the BIM accuracy of cost and time estimation currently, it’s easier than before by Using evolutionary algorithms (EA), which include contemporary algorithms like evolutionary strategies (ES), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE), and genetic algorithms (GA), the integration of artificial intelligence (AI). This study uses a code (plugin) created by GA and integrated into the BIM-5D model via Navisworks as a plugin to reduce the overall time and cost of construction projects. The plugin code, developed with Microsoft Visual Studio and the C# programming language, is an interface that enhances the accuracy of time and cost during construction stages with various five project time scenarios. The study’s findings indicate that the suggested plugin reduces project time by roughly 20% while also saving various amounts of money.

This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

The objective of this paper is to investigate the efficiency of various computational algorithms implemented in the framework of structural optimization methods based on evolutionary algorithms. In particular, the efficiency of parallel computational strategies is examined with reference to evolution strategies (ES) and genetic algorithms (GA). Parallel strategies are implemented both at the level of the optimization algorithm, by exploiting the natural parallelization features of the evolutionary algorithms, as well as at the level of the repeated structural analysis problems that are required by ES and GA. In the latter case the finite element solutions are performed by the FETI domain decomposition method specially tailored to the particular type of problems at hand. The proposed methodology is generic and can be applied to all types of optimization problems as long as they involve large‐scale finite element simulations. The numerical tests of the present study are performed on sizing optimization of skeletal structures. The numerical tests demonstrate the computational advantages of the proposed parallel strategies, which become more pronounced in large‐scale optimization problems. Copyright © 2003 John Wiley & Sons, Ltd.

Conventional implementation of evolutionary programming (EP) for continuous parameter optimization uses Gaussian mutations. However, an implementation of EP with Cauchy mutation operator has empirically been shown to outperform EP using the Gaussian mutations for optimizations of multi-modal functions with many local optima. The faster convergence of EP with Cauchy mutation has been explained in terms of the fatter tails of the Cauchy probability density function, which results in a higher probability of escaping from a local optimum. In this work we present an implementation of EP consisting of a hybrid linear combination of the Cauchy and Gaussian mutations for antenna optimization problems in order to exploit the desirable properties of these two operators. The implementation follows a procedure similar to the one described by Chellapilla (see IEEE Trans. on Evolutionary Computation, September 1998) for function optimization and uses a self-adaptive scheme for updating the standard deviation and the scale parameter of Gaussian and Cauchy distributions, respectively, during the evolution. As an example the optimization of a six-element Yagi-Uda array of dipoles using the hybrid method is presented.

A new gumption approach for economic dispatch problem with losses effect based on valve-point active power