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21 pages, 8146 KiB

Open AccessArticle

Inverse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution and Jacobian Pseudoinverse Approach

by Jesus Hernandez-Barragan, Josue Plascencia-Lopez, Michel Lopez-Franco, Nancy Arana-Daniel and Carlos Lopez-Franco

Algorithms 2024, 17(10), 454; https://doi.org/10.3390/a17100454 - 12 Oct 2024

Viewed by 769

Abstract

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, [...] Read more.

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor generalization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipulators using a metaheuristic algorithm enhanced with a Jacobian step. Our method overcomes those limitations by selectively applying the Jacobian step to the differential evolution (DE) algorithm. The effectiveness and versatility of the proposed approach are demonstrated through simulations and real-world experimentation on a 5 DOF KUKA robotic arm. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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18 pages, 3431 KiB

Open AccessArticle

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

by Vasileios Vasileiadis, Christos Kyriklidis, Vayos Karayannis and Constantinos Tsanaktsidis

Algorithms 2024, 17(5), 181; https://doi.org/10.3390/a17050181 - 28 Apr 2024

Viewed by 1476

Abstract

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of [...] Read more.

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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15 pages, 2722 KiB

Open AccessArticle

Optimizing Speech Emotion Recognition with Deep Learning and Grey Wolf Optimization: A Multi-Dataset Approach

by Suryakant Tyagi and Sándor Szénási

Algorithms 2024, 17(3), 90; https://doi.org/10.3390/a17030090 - 20 Feb 2024

Viewed by 1874

Abstract

Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers to identify and understand human emotions from speech. These technologies contribute to [...] Read more.

Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers to identify and understand human emotions from speech. These technologies contribute to the creation of innovative human–computer interaction (HCI) applications. Deep learning algorithms, capable of learning high-level features directly from raw data, have given rise to new emotion recognition approaches employing models trained on advanced speech representations like spectrograms and time–frequency representations. This study introduces CNN and LSTM models with GWO optimization, aiming to determine optimal parameters for achieving enhanced accuracy within a specified parameter set. The proposed CNN and LSTM models with GWO optimization underwent performance testing on four diverse datasets—RAVDESS, SAVEE, TESS, and EMODB. The results indicated superior performance of the models compared to linear and kernelized SVM, with or without GWO optimizers. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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20 pages, 3125 KiB

Open AccessArticle

CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments

by Alireza Rezvanian, S. Mehdi Vahidipour and Ali Mohammad Saghiri

Algorithms 2024, 17(1), 18; https://doi.org/10.3390/a17010018 - 30 Dec 2023

Cited by 1 | Viewed by 1907

Abstract

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune [...] Read more.

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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17 pages, 540 KiB

Open AccessArticle

Carousel Greedy Algorithms for Feature Selection in Linear Regression

by Jiaqi Wang, Bruce Golden and Carmine Cerrone

Algorithms 2023, 16(9), 447; https://doi.org/10.3390/a16090447 - 19 Sep 2023

Viewed by 1650

Abstract

The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm. In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features. More specifically, we introduce a default version of [...] Read more.

The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm. In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features. More specifically, we introduce a default version of CG that has several novel features. We compare its performance against stepwise regression and more sophisticated approaches using integer programming, and the results are encouraging. For example, CG consistently outperforms stepwise regression (from our preliminary experiments, we see that CG improves upon stepwise regression in 10 of 12 cases), but it is still computationally inexpensive. Furthermore, we show that the approach is applicable to several more general feature selection problems. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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23 pages, 3701 KiB

Open AccessArticle

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

by Juan F. Guerra, Ramon Garcia-Hernandez, Miguel A. Llama and Victor Santibañez

Algorithms 2023, 16(8), 393; https://doi.org/10.3390/a16080393 - 21 Aug 2023

Cited by 2 | Viewed by 1810

Abstract

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle [...] Read more.

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters

P_{0}

,

Q_{0}

, and

R_{0}

are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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26 pages, 18314 KiB

Open AccessArticle

Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins

by Cihan Ates, Dogan Bicat, Radoslav Yankov, Joel Arweiler, Rainer Koch and Hans-Jörg Bauer

Algorithms 2023, 16(8), 387; https://doi.org/10.3390/a16080387 - 12 Aug 2023

Cited by 1 | Viewed by 2376

Abstract

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown [...] Read more.

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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15 pages, 1061 KiB

Open AccessArticle

Improvement of Ant Colony Algorithm Performance for the Job-Shop Scheduling Problem Using Evolutionary Adaptation and Software Realization Heuristics

by Pavel V. Matrenin

Algorithms 2023, 16(1), 15; https://doi.org/10.3390/a16010015 - 26 Dec 2022

Cited by 11 | Viewed by 2975

Abstract

Planning tasks are important in construction, manufacturing, logistics, and education. At the same time, scheduling problems belong to the class of NP-hard optimization problems. Ant colony algorithm optimization is one of the most common swarm intelligence algorithms and is a leader in solving [...] Read more.

Planning tasks are important in construction, manufacturing, logistics, and education. At the same time, scheduling problems belong to the class of NP-hard optimization problems. Ant colony algorithm optimization is one of the most common swarm intelligence algorithms and is a leader in solving complex optimization problems in graphs. This paper discusses the solution to the job-shop scheduling problem using the ant colony optimization algorithm. An original way of representing the scheduling problem in the form of a graph, which increases the flexibility of the approach and allows for taking into account additional restrictions in the scheduling problems, is proposed. A dynamic evolutionary adaptation of the algorithm to the conditions of the problem is proposed based on the genetic algorithm. In addition, some heuristic techniques that make it possible to increase the performance of the software implementation of this evolutionary ant colony algorithm are presented. One of these techniques is parallelization; therefore, a study of the algorithm’s parallelization effectiveness was made. The obtained results are compared with the results of other authors on test problems of scheduling. It is shown that the best heuristics coefficients of the ant colony optimization algorithm differ even for similar job-shop scheduling problems. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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17 pages, 3191 KiB

Open AccessArticle

Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis

by Jozsef Pap, Csaba Mako, Miklos Illessy, Zef Dedaj, Sina Ardabili, Bernat Torok and Amir Mosavi

Algorithms 2022, 15(9), 300; https://doi.org/10.3390/a15090300 - 26 Aug 2022

Cited by 4 | Viewed by 4069

Abstract

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used [...] Read more.

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that “skills requirements and skill matching” and “employee voice” are the two factors that matter most in enhancing firm performance and wellbeing. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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16 pages, 619 KiB

Open AccessArticle

Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion

by Kara Layne Johnson and Nicole Bohme Carnegie

Algorithms 2022, 15(2), 45; https://doi.org/10.3390/a15020045 - 28 Jan 2022

Cited by 2 | Viewed by 2897

Abstract

Genetic algorithms mimic the process of natural selection in order to solve optimization problems with minimal assumptions and perform well when the objective function has local optima on the search space. These algorithms treat potential solutions to the optimization problem as chromosomes, consisting [...] Read more.

Genetic algorithms mimic the process of natural selection in order to solve optimization problems with minimal assumptions and perform well when the objective function has local optima on the search space. These algorithms treat potential solutions to the optimization problem as chromosomes, consisting of genes which undergo biologically-inspired operators to identify a better solution. Hyperparameters or control parameters determine the way these operators are implemented. We created a genetic algorithm in order to fit a DeGroot opinion diffusion model using limited data, making use of selection, blending, crossover, mutation, and survival operators. We adapted the algorithm from a genetic algorithm for design of mixture experiments, but the new algorithm required substantial changes due to model assumptions and the large parameter space relative to the design space. In addition to introducing new hyperparameters, these changes mean the hyperparameter values suggested for the original algorithm cannot be expected to result in optimal performance. To make the algorithm for modeling opinion diffusion more accessible to researchers, we conduct a simulation study investigating hyperparameter values. We find the algorithm is robust to the values selected for most hyperparameters and provide suggestions for initial, if not default, values and recommendations for adjustments based on algorithm output. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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16 pages, 2137 KiB

Open AccessArticle

Parameter Optimization of Active Disturbance Rejection Controller Using Adaptive Differential Ant-Lion Optimizer

by Qibing Jin and Yuming Zhang

Algorithms 2022, 15(1), 19; https://doi.org/10.3390/a15010019 - 5 Jan 2022

Cited by 3 | Viewed by 2749

Abstract

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. [...] Read more.

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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17 pages, 9032 KiB

Open AccessArticle

Behavior Selection Metaheuristic Search Algorithm for the Pollination Optimization: A Simulation Case of Cocoa Flowers

by Willa Ariela Syafruddin, Rio Mukhtarom Paweroi and Mario Köppen

Algorithms 2021, 14(8), 230; https://doi.org/10.3390/a14080230 - 31 Jul 2021

Cited by 2 | Viewed by 2899

Abstract

Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we [...] Read more.

Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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Review

Jump to: Research

32 pages, 4797 KiB

Open AccessReview

Generative Adversarial Network for Overcoming Occlusion in Images: A Survey

by Kaziwa Saleh, Sándor Szénási and Zoltán Vámossy

Algorithms 2023, 16(3), 175; https://doi.org/10.3390/a16030175 - 22 Mar 2023

Cited by 4 | Viewed by 4558

Abstract

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded [...] Read more.

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. The generative adversarial network (GAN), on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions. In this survey, we outline the existing works wherein GAN is utilized in addressing the challenges of overcoming occlusion, namely amodal segmentation, amodal content completion, order recovery, and acquiring training data. We provide a summary of the type of GAN, loss function, the dataset, and the results of each work. We present an overview of the implemented GAN architectures in various applications of amodal completion. We also discuss the common objective functions that are applied in training GAN for occlusion-handling tasks. Lastly, we discuss several open issues and potential future directions. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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