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48 pages, 16863 KiB

Open AccessArticle

Multi-Strategy Hybrid Whale Optimization Algorithm Improvement

by Xie Xie, Yulin Yang and Huan Zhou

Appl. Sci. 2025, 15(4), 2224; https://doi.org/10.3390/app15042224 - 19 Feb 2025

Viewed by 703

The whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm developed by Mirjalili and Lewis in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, scholars have adopted this algorithm to address various problems in different [...] Read more.

The whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm developed by Mirjalili and Lewis in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, scholars have adopted this algorithm to address various problems in different disciplines. However, standard WOA has the problems of slow convergence speed, insufficient search accuracy, and limited ability to solve complex problems. In order to solve these problems, this paper proposes a multi-strategy hybrid whale algorithm (MHWOA). Firstly, the calculation speed is accelerated by modifying the parameters; then, the accuracy of the algorithm is improved by incorporating the scatter search strategy; finally, the simulated annealing algorithm is integrated to improve its ability to solve complex problems. The performance differences between MHWOA, the baseline algorithm, and the improved WOA algorithm are compared using the CEC2017 test suite and three real-world engineering problems. In the comparison of processing results of various problems, the calculation accuracy of MHWOA is improved by no less than 1.96%, the calculation error is reduced by no less than 1.83%, and the execution time is improved by no less than 5.6%. In the CNN-MHWOA-based time series electricity load forecasting problem, MHWOA shows the advantages of reduced error and improved fitting degree with the true value compared with the standard WOA. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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16 pages, 350 KiB

Open AccessArticle

Bias and Variance Analysis of Contemporary Symbolic Regression Methods

by Lukas Kammerer, Gabriel Kronberger and Stephan Winkler

Appl. Sci. 2024, 14(23), 11061; https://doi.org/10.3390/app142311061 - 28 Nov 2024

Viewed by 987

Abstract

Symbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar solution. The implications in practice are [...] Read more.

Symbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar solution. The implications in practice are huge, as interpretability as key-selling feature degrades when minor changes in data cause substantially different behavior of models. We analyse those perturbations caused by changes in training data for ten contemporary symbolic regression algorithms. We analyse existing machine learning models from the SRBench benchmark suite, a benchmark that compares the accuracy of several symbolic regression algorithms. We measure the bias and variance of algorithms and show how algorithms like Operon and GP-GOMEA return highly accurate models with similar behavior despite changes in training data. Our results highlight that larger model sizes do not imply different behavior when training data change. On the contrary, larger models effectively prevent systematic errors. We also show how other algorithms like ITEA or AIFeynman with the declared goal of producing consistent results meet up to their expectation of small and similar models. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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16 pages, 3825 KiB

Open AccessArticle

Evolutionary Grid Optimization and Deep Learning for Improved In Vitro Cellular Spheroid Localization

by Jonas Schurr, Hannah Janout, Andreas Haghofer, Marian Fürsatz, Josef Scharinger, Stephan Winkler and Sylvia Nürnberger

Appl. Sci. 2024, 14(20), 9476; https://doi.org/10.3390/app14209476 - 17 Oct 2024

Viewed by 1112

Abstract

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. [...] Read more.

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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28 pages, 1938 KiB

Open AccessArticle

Unemployment Rate Prediction Using a Hybrid Model of Recurrent Neural Networks and Genetic Algorithms

by Kevin Mero, Nelson Salgado, Jaime Meza, Janeth Pacheco-Delgado and Sebastián Ventura

Appl. Sci. 2024, 14(8), 3174; https://doi.org/10.3390/app14083174 - 10 Apr 2024

Cited by 2 | Viewed by 2846

Abstract

Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to make short-term estimates, assess economic health, and make informed monetary policy decisions. [...] Read more.

Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to make short-term estimates, assess economic health, and make informed monetary policy decisions. This paper proposes the innovative GA-LSTM method, which fuses an LSTM neural network with a genetic algorithm to address challenges in unemployment prediction. Effective parameter determination in recurrent neural networks is crucial and a well-known challenge. The research uses the LSTM neural network to overcome complexities and nonlinearities in unemployment predictions, complementing it with a genetic algorithm to optimize the parameters. The central objective is to evaluate recurrent neural network models by comparing them with GA-LSTM to identify the most appropriate model for predicting unemployment in Ecuador using monthly data collected by various organizations. The results demonstrate that the hybrid GA-LSTM model outperforms traditional approaches, such as BiLSTM and GRU, on various performance metrics. This finding suggests that the combination of the predictive power of LSTM with the optimization capacity of the genetic algorithm offers a robust and effective solution to address the complexity of predicting unemployment in Ecuador. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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24 pages, 15066 KiB

Open AccessArticle

Population Dynamics in Genetic Programming for Dynamic Symbolic Regression

by Philipp Fleck, Bernhard Werth and Michael Affenzeller

Appl. Sci. 2024, 14(2), 596; https://doi.org/10.3390/app14020596 - 10 Jan 2024

Cited by 2 | Viewed by 1956

Abstract

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of [...] Read more.

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS’s unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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18 pages, 644 KiB

Open AccessArticle

Applying Learning and Self-Adaptation to Dynamic Scheduling

by Bernhard Werth, Johannes Karder, Michael Heckmann, Stefan Wagner and Michael Affenzeller

Appl. Sci. 2024, 14(1), 49; https://doi.org/10.3390/app14010049 - 20 Dec 2023

Cited by 1 | Viewed by 2606

Abstract

Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to [...] Read more.

Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to these problems are often very specific to the application case or rely on simple problem formulations with known and stable parameters. This work presents a dynamic scheduling scenario for a production setup where little information about the system is known a priori. Instead of fully specifying all relevant problem data, the timing and batching behavior of machines are learned by a machine learning ensemble during operation. We demonstrate how a meta-heuristic optimization algorithm can utilize these models to tackle this dynamic optimization problem, compare the dynamic performance of a set of established construction heuristics and meta-heuristics and showcase how models and optimizers interact. The results obtained through an empirical study indicate that the interaction between optimization algorithm and machine learning models, as well as the real-time performance of the overall optimization system, can impact the performance of the production system. Especially in high-load situations, the dynamic algorithms that utilize solutions from previous problem epochs outperform the restarting construction heuristics by up to ~24%. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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17 pages, 2342 KiB

Open AccessArticle

Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems

by Francisco J. Soltero, Pablo Fernández-Blanco and J. Ignacio Hidalgo

Appl. Sci. 2023, 13(22), 12485; https://doi.org/10.3390/app132212485 - 19 Nov 2023

Cited by 1 | Viewed by 1579

Abstract

Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in [...] Read more.

Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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18 pages, 3964 KiB

Open AccessArticle

Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture

by Alexander Hinterleitner, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng and Phillip Priss

Appl. Sci. 2023, 13(20), 11506; https://doi.org/10.3390/app132011506 - 20 Oct 2023

Cited by 2 | Viewed by 1780

Abstract

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard [...] Read more.

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks. Full article

(This article belongs to the Special Issue Evolutionary Algorithms and Their Real-World Applications)

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