Use Of Evolutionary Algorithms Research Articles

Optimization of filter structures by evolutionary strategies

The increasing pressure drop of filter media during the capture of solid particles is directly correlated to the energy consumption. It is largely based on the microstructure of the media, especially with regard to the selected porosity gradient. The development of new filter media optimized for separation efficiency and energy consumption is based mainly on experimental work. The use of evolutionary algorithms inspired by nature is of particular interest for the optimization of filter media, as a large number of non-linear influencing factors can be processed. However, a large number of simulations is required for successful application in order to reproduce the natural process of mutation and individual selection. Most numerical simulations of filtration properties are limited due to the high computing power required, making them unsuitable in this context. Therefore, a 1D simulation with low computing power requirements was used in this paper to simulate the loading of a filter medium with particles. This approach enabled the rapid calculation of the loading of different filter media structures, and was coupled with an evolutionary algorithm with the aim of optimizing the porosity gradient. The algorithm was tested on different particle size distributions, which represented different operating conditions. The porosity gradients of the optimized filters was in the form of an inverse square function, leading to a much more homogenous, microscopic loading with particles. Due to the significantly improved utilization of the filter depth compared to a reference medium, a much slower increase in pressure difference could be obtained with a similar filtration efficiency, resulting in increased quality factors of 30% and higher. The optimization of an operating point took about 24 hours on a standard desktop PC and could be extended to other structural parameters of the filter medium apart from the porosity gradient. This shows the potential for a broad application in the selection and optimization of filter media.

Investigation of the Use of Evolutionary Algorithms for Modeling and Simulation of Bifacial Photovoltaic Modules

The purpose of this study is to employ and improve evolutionary algorithms, namely the genetic algorithm (GA) and the differential evolution algorithm (DE), to extract the parameters of the equivalent circuit model (ECM) of a bifacial photovoltaic module using the representative model of a diode with five parameters (1D5P). The objective is to simulate the characteristics of the I–V curves for various irradiation and temperature scenarios. A distinctive feature of this study is the exclusive use of the information in the technical sheet of the bifacial module to conduct the entire extraction and simulation process, eliminating the need to resort to external sources of data or experimental data. To validate the methods, a comparison was made between the simulation results and the data provided by the bifacial module manufacturer, contemplating different scenarios of irradiation and temperature. The DE was the most accurate algorithm for the 1D5P model, which presented a maximum average error of 1.57%. In comparison, the GA presented a maximum average error of 1.98% in the most distant scenario of STC conditions. Despite the errors inherent to the simulations, none of the algorithms presented relative errors greater than 8%, which represents a satisfactory modeling for the different operational conditions of the bifacial photovoltaic modules.

Solving Quadratic Assignment Problem by Using Meta-heuristic Search Method

While analytical solutions to Quadratic Assignment Problems (QAP) have indeed been since a long time, the expanding use of Evolutionary Algorithms (EAs) for similar issues gives a framework for dealing with QAP with an extraordinarily broad scope. The study's key contribution is that it normalizes all of the criteria into a single scale, regardless of their measurement systems or the requirements of minimum or maximum, relieving the researchers of the exhaustively quantifying the quality criteria. A tabu search algorithm for quadratic assignment problems (TSQAP) is proposed, which combines the limitations of tabu search with a discrete assignment problem. The effectiveness of the proposed technique has been compared to well-established alternatives, and its operating principle is illustrated with a numerical example. After repeating the solution of each issue (8) once and recording the algorithm results, it showed its agreement, once from a total (375) repetition of the experiment while the number of times the Artificial Bee Colony (ABC) arrived (2) as for the Firefly (FA) Algorithm giving (117), also Genetic (GA) and Particle Swarm (PSO) gives (120) and the Tabu Search algorithm (174). The proposed technique (TSQAP) is shown to yield a superior solution with low computing complexity. MATLAB was used to generate all of the findings (R2020b).

Beyond passive House: use of evolutionary algorithms in architectural design

As buildings are accountable for one-fifth of the total greenhouse gas emissions, improving their energy efficiency is foreseen as a pivotal opportunity towards climate neutrality, answering the UN call for climate action. Thus, this work intends to study to what extent the optimisation of the geometrical characteristics of building envelope can contribute to reducing their energy consumption under different climatic scenarios. The case studies have focused on three single-family houses located in different climatic zones in southern Brazil. Therefore, an evolutionary algorithm was employed in a two-step optimisation approach. First the window area and secondly the shading solutions dimensions, considering the Passive House (PH) concept requirements. The optimisation of the window areas and shading solutions dimensions together with the PH concept resulted in a reduction of energy demand between 76 and 91% and an increase in thermal comfort between 89 and 93%. Regarding window dimensions, all scenarios experienced a significant reduction of glazed areas, from 38.9 to 60.5%. As conclusion, an adequate design of shading elements and windows significantly contribute to improve indoor thermal comfort and to reduce the buildings’ energy demand, an utmost relevant factor to be considered in the early stages of project design pursuing high energy performance buildings.

Evolutionary Algorithm in Game – A Systematic Review

Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.

The Use of Evolutionary Algorithms in the Modelling of Diffuse Radiation in Terms of Simulating the Energy Efficiency of Photovoltaic Systems

In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of total and diffuse instantaneous solar radiation; however, most meteorological stations conduct actinometric measurements only with regard to total solar radiation, especially on a minute scale. This paper contains an analysis of the currently used PV cell mathematical model and suggests its modification aimed at calculating PV cell power with satisfactory accuracy, without the knowledge of diffuse solar radiation. Three function families were proposed to approximate the relationship between diffuse irradiance and the total and theoretical total irradiance variance for a cloudless sky. A program has been implemented to identify functions from the aforementioned function families. It leverages an evolution strategy algorithm and a fitness function based on the least-squares point method. It was employed to calculate the desired functions based on actual measurement data. The outcome was the sought-after dependence that enables predicting diffuse irradiance based on more frequently available measurement data.

Toward human-level tonal and modal melody harmonizations

This paper considers the problem of automatic music generation in the form of chord voicings, which is an important part of melody harmonization, with the use of Evolutionary Algorithms (EA), based on the rules derived from music theory and practice. The rules, whose role is to ensure the fulfillment of both the formal requirements of chord voicings and its less-formalized aesthetic requirements are encoded in the fitness function of EA. The fitness function is composed of several modules, each of which consists of smaller parts corresponding to the implemented music rules. The above modular design allows for flexible modification and extension of this function. The way the fitness function is constructed and tuned for better chord voicings quality is discussed in the context of music theory and technical EA implementation. In particular, we show how could generated voices be modeled by means of adjusting the relevance of particular fitness function components or extended by adding new components to the fitness function. The proposed algorithm is tested on two types of music: tonal and modal. Although tonal and modal chord voicings are significantly different, the achieved results (assessed by a human expert) indicate that the obtained solutions are both technically and aesthetically correct (i.e. they adhere to the theoretical rules and are nice to listen to). This study extends our previously published conference paper (Mycka et al., 2022).

Evolution-by-Coevolution of Neural Networks for Audio Classification

Neural networks are increasingly used in recognition problems, including static and moving images, sounds, etc. Unfortunately, the selection of optimal neural network architecture for a specific recognition problem is a difficult task, which often has an experimental nature. In this paper we present the use of evolutionary algorithms to obtain optimal architectures of neural networks used for audio sample classification. We extend the Pytorch DNN Evolution tool implementing co-evolutionary algorithms which create groups of neural networks that solve a given problem with a certain accuracy, with the support for problems in which training data consists of audio samples. In this paper we use the co-evolutionary approach to solve a sample sound classification problem. We describe how the sound data was prepared for processing with the use of the Mel Frequency Cepstral Coefficients (MFCC). Next we present the results of experiments conducted with the AudioMnist dataset. The obtained neural network architectures, whose classification accuracy is comparable to the classification accuracy attained by the AlexNet neural network, and their implications are discussed.

Benchmarking Differential Evolution on a Quantum Simulator

The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradigm are selection of ’Fit’ individuals (from a population sample) for retention, cloning, mutation, discarding, breeding, crossover etc. In the Evolutionary Algorithm abstraction, the individuals are deemed to be solution candidates to an Optimization problem and additional solution(/sets) are built by applying analogies to the above primitives (cloning, mutation etc.) by means of evaluating a ’Fitness’ function/criterion. One such algorithm is Differential Evolution(DE) which can be used to compute the minima of functions such as the rastrigin function and rosenbrock function. This work is an attempt to study the result of applying the DE method on these functions with candidate individuals generated on classical Turing modeled computation and comparing the same with those on state of the art Quantum computation.The study benchmarks the convergence of these functions by varying the parameters initialized and reports timing, convergence, and resource utilization results.

Hybrid Niching-Based Differential Evolution With Two Archives for Nonlinear Equation System

Nonlinear equation systems (NESs) are ubiquitous, and solving them is an important yet challenging task in numerical computation. Most of the NESs usually contain multiple roots. To locate multiple roots, the use of evolutionary algorithms attracts more attention recently. For the purpose of finding different roots within a limited computational budget in a single run simultaneously, in this article, a hybrid niching-based differential evolution with two archives, referred to as HNDE/2A, is proposed. It can be featured as: 1) two niching techniques, i.e., crowding and speciation, are combined to balance the diversity and convergence; 2) a root archive is used to save the found roots during the run. Additionally, if a root is saved into the root archive, it will be reinitialized immediately to further promote the population diversity; and 3) an inferior offspring archive is presented to utilize the useful information of the inferior offspring. To evaluate the performance of our proposal, 30 problems are chosen as the test suite. Moreover, HNDE/2A is also used to solve two real-world problems. Compared with other algorithms, HNDE/2A yields better results in terms of the root ratio and the success rate with a less computational budget.

Use of Evolutionary Algorithm for Identifying Quantitative Impact of PM2.5 and PM10 on PV Power Generation

This publication presents the impact of PM10, PM2.5, and cloudiness on the power that is generated by photovoltaic panels—the actual photovoltaic power was measured. Weather parameters that were recorded by a weather station were taken into account, and the dependencies between the weather parameters and the power that was generated by PV panels were determined. This study was based on actual data from a solar cell set and was designed to allow a certain size of a PV system to be able to supply power to a given load. For the entire measurement year, data on PM10, PM2.5, cloudiness, and generated power were collected; by using a genetic algorithm, the influence of the environmental parameters on the power that was generated by the PV panels was calculated. The research shows the influence of anthropogenic factors on the power that is generated by PV panels. It was observed that PM2.5 and PM10 air pollution decreased the power by about 16% among the analyzed factors as they were related to cloudiness. The impact of the pollution was stable over the year in the analyzed location.

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

The use of evolutionary algorithms (EAs) for solving complex engineering problems has been very promising, so the application of EAs for optimal operation of hydropower reservoirs can be of great help. Accordingly, this study investigates the capability of 14 recently-introduced robust EAs in optimization of energy generation from Karun-4 hydropower reservoir. The best algorithm is the one that produces the largest objective function (energy generation) and has the minimum standard deviation (SD), the minimum coefficient of variations (CV), and the shortest time of CPU usage. It was found that the best solution was achieved by the moth swarm algorithm (MSA), with the optimized energy generation of 19,311,535 MW which was 65.088% more than the actual energy generation (11,697,757). The values of objective function, SD and CV for MSA were 0.147, 0.0029 and 0.0192, respectively. The next ranks were devoted to search group algorithm (SGA), water cycle algorithm (WCA), symbiotic organism search algorithm (SOS), and coyote optimization algorithm (COA), respectively, which have increased the energy generation by more than 65%. Some of the utilized EAs, including grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), antlion optimization algorithm (ALO), and whale optimization algorithm (WOA), failed to produce reasonable results. The overall results indicate the promising capability of some EAs for optimal operation of hydropower reservoirs.

The Automatic Design of Multimode Resonator Topology with Evolutionary Algorithms.

Microwave electromagnetic devices have been used for many applications in tropospheric communication, navigation, radar systems, and measurement. The development of the signal preprocessing units including frequency-selective devices (bandpass filters) determines the reliability and usability of such systems. In wireless sensor network nodes, filters with microstrip resonators are widely used to improve the out-of-band suppression and frequency selectivity. Filters based on multimode microstrip resonators have an order that determines their frequency-selective properties, which is a multiple of the number of resonators. That enables us to reduce the size of systems without deteriorating their selective properties. Various microstrip multimode resonator topologies can be used for both filters and microwave sensors, however, the quality criteria for them may differ. The development of every resonator topology is time consuming. We propose a technique for the automatic generation of the resonator topology with required frequency characteristics based on the use of evolutionary algorithms. The topology is encoded into a set of real valued parameters, which are varied to achieve the desired features. The differential evolution algorithm and the genetic algorithm with simulated binary crossover and polynomial mutation are applied to solve the formulated problem using the dynamic penalties method. The experimental results show that our technique enables us to find microstrip resonator topologies with desired amplitude-frequency characteristics automatically, and manufactured devices demonstrate characteristics very close to the results of the algorithm. The proposed algorithmic approach may be used for automatically exploring the new perspective topologies of resonators used in microwave filters, radar antennas or sensors, in accordance with the defined criteria and constraints.

A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

A Machine-Learning-Based Labelling Diversity Model for Predictive Analysis: Using 16QAM as a Case Study

The recent advancement and enhancement that optimized uncoded space-time labelling diversity (USTLD) have provided significant diversity gains. By adopting the use of evolutionary algorithms, labelling diversity (LD) mapper designs produced are near-optimal in quality. The only disadvantage to the use of evolutionary algorithms is that the produced solution is not always optimal. To ease the calculation of how much a mapper design had achieved LD, this paper proposes a machine learning-based analysis to predict the amount of LD achieved by a mapper. In this paper, only the 16QAM constellation is studied as a simple case. Six machine learning-based algorithms were proposed in this paper, namely multi-linear regression (MLR), support vector regression (SVR), decision trees (DT), random forest (RF), K-nearest neighbours (KNN) and a simple artificial neural network (ANN). From the results obtained from the experiments, it can be seen that the MLR algorithm is the least time complex while the ANN is the most time complex. It is also important to note that the DT and KNN algorithms take a comparatively short amount of time to execute. When compared in terms of machine learning metrics, it was shown that the ANN algorithm performed the best with the least amount of error while the MLR algorithm performed the worst with the highest amount of error. Thus, it could be seen that the results from this paper provide a positive outlook on applying machine learning algorithms to the LD problem.

An Empirical Study on How Sapienz Achieves Coverage and Crash Detection

AbstractSeveral tools for automatically testing Android applications have been proposed. In particular, Sapienz is a search‐based tool that has been recently deployed in an industrial setting. Although it has been shown that Sapienz outperforms several state‐of‐the‐art tools, it is still to be seen what features of SAPIENZ impact the most on its effectiveness. We conducted an extensive empirical study where we compare the impact of the search algorithm and the usage of motif genes, a more compact representation of individuals. Our empirical study shows that the usage of motif genes improves coverage both for Evolutionary Algorithms and random approaches. In particular, it also shows that NSGA‐II, the multi‐objective evolutionary algorithm used by Sapienz, does not have a clear improvement over other algorithms. In terms of number of crashes detected, our study shows that both NSGA‐II and Random Search perform similarly. While the usage of motif genes improves the crash detection of algorithms, it is not enough to make it statistically significant. These facts cast doubts about the use of Evolutionary Algorithms in the context of Android test generation and suggest that motif genes can have a great impact on the overall effectiveness.

EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning

Missing data is a considerable problem in knowledge extraction where the completeness and the quality of the data play a major role in data analysis. In many applications, ignoring the records with missing values may adversely affect the prediction process and creates a significant bias in the resulting data. Therefore, Missing Data Imputation (MDI) has become mandatory to tackle the negative consequences of the presence of missing data. However, different features show different behaviours to data imputation, as the imputation of some features can enhance the learning process while others may lead to worse results according to the feature properties. This paper proposes the use of evolutionary algorithms to evaluate the usefulness of the imputation for each feature on the performance of the prediction model, in order to select the best subset of incomplete features that can enhance the learning process and maximize the prediction power of the model after it has been handled properly. This paper proposes a new approach for handling missing values while performing feature selection simultaneously to enhance the model’s learning performance and reduce the negative consequences of imputation. The performance of the proposed method was evaluated using 10 bench-marking datasets under 10-folds cross validation test. The results were compared with five classical imputation methods (mean, median, multiple imputation, expectation maximization, and K-nearest neighbours). The proposed methodology significantly outperformed other methods in terms of accuracy, sensitivity, specificity, geometric means, and the area under the curve. Moreover, the effectiveness of the proposed method was compared against three recent evolutionary based imputation methods, where the proposed methodology outperformed other methods in terms of accuracy in 75% of the datasets.

QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.

A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

Community detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale networks has gained considerable popularity because these algorithms are fairly effective in networks with a relatively small number of nodes. In this paper, we propose a parallel multi-objective EA, called PMOEA, for community detection in large-scale networks, where the communities associated with key network nodes are detected in parallel. Specifically, we develop a multi-objective and a single-objective EA. The former is used to detect the communities of a key node instead of all communities in the network. The latter obtains the communities in the entire network using the previously detected communities of each key node. The performance of the proposed method was verified on both large-scale synthetic benchmark networks and real-world networks. The results demonstrated the superiority of PMOEA over six EA-based and two non-EA-based community-detection algorithms for large-scale networks.

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

This paper investigates the use of evolutionary algorithms for the optimization of time-constrained impulsive multirendezvous missions. The aim is to find the minimum- Δ V trajectory that allows a chaser spacecraft to perform, in a prescribed mission time, a complete tour of a set of targets, such as space debris or artificial satellites, which move on the same orbital plane at slightly different altitudes. For this purpose, a two-level design approach is pursued. First, an outer-level combinatorial problem is defined, dealing with the simultaneous optimization of the sequence of targets and the rendezvous epochs. The suggested approach is first tested by assuming that all transfer legs last exactly the same amount of time; then, the time domain is discretized over a finer grid, allowing a more appropriate sizing of the time window allocated for each leg. The outer-level problem is solved by an in-house genetic algorithm, which features an effective permutation-preserving solution encoding. A simple, but fairly accurate, heuristic, based on a suboptimal four-impulse analytic solution of the single-target rendezvous problem, is used when solving the combinatorial problem for a fast guess at the transfer cost, given the departure and arrival epochs. The outer-level problem solution is used to define an inner-level NLP problem, concerning the optimization of each body-to-body transfer leg. In this phase, the encounter times are further refined. The inner-level problem is tackled through an in-house multipopulation self-adaptive differential evolution algorithm. Numerical results for case studies including up to 20 targets with different time grids are presented.