Evolutionary Computation Research Articles

A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA.

Evolutionary Computing Control Strategy of Nonholonomic Robots with Ordinary Differential Equation Kinematics Model

This paper introduces an Evolutionary Computing Control Strategy (ECCS) for the motion control of nonholonomic robots, and integrates an ordinary differential equation (ODE)-based kinematics model with a nonlinear model predictive control (NMPC) strategy and a particle-based evolutionary computing (PEC) algorithm. The ECCS addresses the key challenges of traditional NMPC controllers, such as their tendency to fall into local optima when solving nonlinear optimization problems, by leveraging the global optimization capabilities of evolutionary computation. Experiment results on the MATLAB Simulink platform demonstrate that the proposed ECCS significantly improves motion control accuracy and reduces control errors compared to linearized MPC (LMPC) strategies. Specifically, the ECCS reduces the maximum error by 90.6% and 94.5%, the mean square error by 67.8% and 92.6%, and the root mean square error by 43.5% and 70.3% in velocity control and steering angle control, respectively. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. These results validate the effectiveness of embedding ODE kinematics into the evolutionary computing framework for robust and efficient motion control of nonholonomic robots.

Calculation of low-temperature stacking-fault energy and microstructural evolution of high-manganese steels

Calculation of low-temperature stacking-fault energy and microstructural evolution of high-manganese steels

A Semi-analytical Model for Stellar Evolution in AGN Disks

Abstract Disks of gas accreting onto supermassive black holes may host numerous stellar-mass objects, formed within the disk or captured from a nuclear star cluster. We present a simplified model of stellar evolution in these dense environments, which exhibits exceptional agreement with full stellar evolution calculations at a minuscule fraction of the cost. Although the model presented here is limited to stars burning hydrogen in their cores, it is sufficient to determine the evolutionary fate of disk-embedded stars: whether they proceed to later stages of nuclear burning and leave behind a compact remnant, reach a quasi-steady state where mass loss and accretion balance one another, or whether accretion proceeds faster than stellar structure can adjust, causing a runaway. We highlight how various disk parameters and phenomena such as gap opening affect stellar evolution outcomes. We also highlight how our model can accommodate time-varying conditions, such as those experienced by a star on an eccentric orbit, and can couple to N-body integrations. This model will enable more detailed studies of stellar populations and their interaction with accretion disks than have previously been possible.

Exploration-Exploitation Tradeoffs in Metaheuristics: A Review

A metaheuristic algorithm is an algorithmic framework at a high level, independent of problems, which offers a set of recommendations or strategies to develop heuristic optimization algorithms. Evolutionary computation and other metaheuristics have long been focused on the trade-off between exploration and exploitation. This subject is present in a wide range of domains, including modeling and prediction, search and optimization, machine learning and cognition, and many more situations where uncertainty is present. The trade-off between exploration and exploitation is crucial for all optimization techniques. Efficient optimization and lower computing costs can be achieved by striking a fair balance between both. The paper introduces a study that discusses metaheuristic algorithms and previous studies in three areas: 1) What elements of metaheuristic facilitate exploration and exploitation; 2) When and how exploration control is applied. 3) how to find a balance between the two.

The Impact of Mobile Technology on English Writing Teaching: The Relationship between Interactive Feedback and Autonomous Learning Abilities

With the rapid development of information technology (IT), mobile technology has been widely applied in the field of education, particularly in language learning. English writing, as one of the core skills in language acquisition, faces numerous challenges within traditional teaching models, such as limited learner autonomy and the difficulty of meeting individualized needs. In recent years, mobile technology-assisted English writing teaching has become a new research focus, with interactive feedback mechanisms and the enhancement of autonomous learning abilities being identified as key factors influencing instructional effectiveness. This study aims to explore mobile network-based English writing teaching, specifically analyzing how locationaware features and interactive feedback can enhance students’ autonomous learning abilities. In particular, the study investigates the forms and challenges of mobile network-assisted English writing teaching, examining mobile network discovery technologies, similarity calculation, and evolutionary computation methods relevant to English writing teaching, and proposes strategies for enhancing autonomous learning through interactive feedback.

Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature. In the evolutionary computation community, the so-called decomposition-basedmethods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple sub-problems to be solved collaboratively. Various interactive versions of decomposition-based methods have been proposed. However, most of them do not consider the desirable properties of real interactive solution processes, such as avoiding imposing a high cognitive burden on the decision-maker, allowing them to decide when to interact with the method, and supporting them in selecting a final solution. This paper reviews interactive evolutionary decomposition-based multiobjective optimization methods and different methodologies utilized to incorporate interactivity in them. Additionally, desirable properties of interactive decomposition-based multiobjective evolutionary optimization methods are identified, aiming to make them easier to be applied in real-world problems.

An adaptive matrix-based evolutionary computation framework for EEG feature selection

An adaptive matrix-based evolutionary computation framework for EEG feature selection

MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

Producing Type Ia Supernovae from Hybrid CONe White Dwarfs with Main-sequence Binary Companions at Low Metallicity of Z = 0.0001

Abstract The nature of progenitors of Type Ia supernovae (SNe Ia) and their explosion mechanism remains unclear. It has been suggested that SNe Ia may have resulted from thermonuclear explosions of hybrid carbon–oxygen–neon white dwarfs (CONe WDs) when they grow in mass to approach the Chandrasekhar mass limit by accreting matter from a binary main-sequence (MS) companion. In this work, we combine the results of detailed binary evolution calculations with population synthesis models to investigate the rates and delay times of SNe Ia in the CONe WD + MS channel at a low metallicity environment of Z = 0.0001. For a constant star formation rate of 5 M ⊙ yr−1, our calculations predict that the SN Ia rates in the CONe WD + MS channel at low metallicity of Z = 0.0001 is about 0.11−3.89 × 10−4 yr−1. In addition, delay times in this channel cover a wide range of 0.05−2.5 Gyr. We further compare our results to those given by a previous study for the CONe WD + MS channel with a higher metallicity of Z = 0.02 to explore the influence of metallicity on the results. We find that these two metallicity environments give a slight difference in rates and delay times of SNe Ia from the CONe WD + MS channel, although SNe Ia produced at a low metallicity environment of Z = 0.0001 have relatively longer delay times.

Комбинированный метод планирования релизов в Agile-командах на основании генетических алгоритмов

The paper analyzes the practices of release planning and task allocation in agile project management using multi-criteria optimization methods and genetic algorithms. A combined method that solves the problem of selecting re-quirements for release (Next Release Problem) based on NSGA-II algorithm and optimization of task scheduling con-sidering resource constraints and sequence of stages (Resource Constrained Project Scheduling Problem) is developed. The peculiarities of using DEAP library in Python to implement evolutionary computations are described, as well as the use of NumPy library to accelerate vector operations and Matplotlib to visualize the results. The architecture of the software solution is presented, including modular code organization and integration of custom heuristics for building optimal schedules. The description of utility, risk and penalty functions for dependency violations and resource con-straints is given, as well as the application of crossover and mutation methods for dictionary structures of schedules. Iterative optimization linking the stages of epics selection and schedule construction to reach a stable optimum is shown. Experimental validation confirmed the effectiveness of the proposed approach as it achieves utility maximiza-tion of the set of epics, minimization of penalties and idle time of employees in constructing optimal schedules. The developed system is applicable for complex release scheduling in Agile teams and forms the basis for further research in agile project management and resource optimization.

Application of Interactive Evolutionary Computing in Personalized Interior Design

Design is a complex and clever process with multiple concepts attempting to explain it. To validate ideas and quantitatively characterize the design process, design experiments, and data analysis are required and predictable. Applying data analysis in design experiments can be challenging, and data are often underutilized. In this study, we develop an experimentation Interactive Evolutionary Computing Design (IECD) model using Upgraded Particle Swarm Optimization (UPSO) within the personalized interior design to iteratively analyze the new color combinations based on 116 designer’s preferences. The study proposed data-infused research into interior design via experimental techniques and the proposal system is evaluated, with additional feedback collected from experimented 116 designers, and data visualization analysis using Color Histogram Analysis and Auto encoders for data’s structure and patterns of the data, thus enhancing user preferences. A proposal system is developed using the collected data to suggest design options that align with individual user preferences and then integrated into the IECD framework to generate personalized design solutions that are more efficient and intelligent. The results are the effectiveness of data analysis and visualization in revealing design patterns and the impact of the data-infused IECD system to enhance the personalization and efficiency of interior design solutions. The IECD system enhances interior design efficiency and personalization through a proposal system, revealing design patterns, and aligning options with individual user preferences.

Phase-field modeling of the morphological and thermal evolution of additively manufactured polylactic acid layers and their influence on the effective elastic mechanical properties

AbstractThis study presents a comprehensive simulation of the fused deposition modeling (FFF) process of polylactic acid (PLA) using the multiphase-field method. Compared to existing works, this work aims to simulate the overall FFF process. It combines temperature evolution, viscous flow, polymer crystallization, and residual strain calculations within the microstructure with mechanical property analysis in a single study. Simulation studies were done in the case of the single layer to study the flowing effect of the filament and the distribution of temperature, viscosity, and relative crystallinity throughout the cooling process. Afterward, a system of layers with three rows and three columns was investigated. The nozzle temperature, bed temperature, viscosity, and layer height were varied, and for each case the porosity was calculated. After running mechanical loading simulations on each case, the effective Young’s modulus was calculated. The simulations show that increasing the nozzle and bed temperatures leads to a decrease in the porosity, while increasing the layer height increases the distortion in the pores’ shapes without significantly affecting the porosity. The decrease in porosity leads to an increase in the effective Young’s modulus of the structure in a linear trend within the investigated porosities. The Young’s modulus–porosity relation was validated with experimental values from the literature within an average error of 3.6 %.

Network Dismantling on Signed Network by Evolutionary Deep Reinforcement Learning.

Network dismantling is an important question that has attracted much attention from many different research areas, including the disruption of criminal organizations, the maintenance of stability in sensor networks, and so on. However, almost all current algorithms focus on unsigned networks, and few studies explore the problem of signed network dismantling due to its complexity and lack of data. Importantly, there is a lack of an effective quality function to assess the performance of signed network dismantling, which seriously restricts its deeper applications. To address these questions, in this paper, we design a new objective function and further propose an effective algorithm named as DSEDR, which aims to search for the best dismantling strategy based on evolutionary deep reinforcement learning. Especially, since the evolutionary computation is able to solve global optimization and the deep reinforcement learning can speed up the network computation, we integrate it for the signed network dismantling efficiently. To verify the performance of DSEDR, we apply it to a series of representative artificial and real network data and compare the efficiency with some popular baseline methods. Based on the experimental results, DSEDR has superior performance to all other methods in both efficiency and interpretability.

Applying Multi-Objective Evolutionary Computation for Optimal Feng Shui Layout in Interior Design

Feng Shui principles have a profound impact in Asia, studies have shown that consumers often consider Feng Shui when purchasing property to arrange interior layouts. Balancing design requirements and cultural beliefs in the design process requires significant communication and calculation efforts, However, aside from repeated communication among Feng Shui experts, homeowners, and designers, there is currently a lack of efficient methods to incorporate Feng Shui into design. Therefore, this study establishes a decision model to provide layout recommendations for purchase property, design, and for existing property renovation planning. By references Feng Shui Compass School principles to assess the Feng Shui quality of dwelling interiors and considers spatial layout and area distribution rules to evaluate the feasibility of the solution. Multi-Objective Evolutionary Algorithm (MOEA) is then applied to optimize Feng Shui and design conditions in real-world case studies. The results show that the application can effectively optimize and balance Feng Shui and design conditions in a short period of time, also provides homeowners and designers with clear strategies during purchase, design, and renovation to meet the needs related to cultural beliefs.

Enhancing neural network applications in phonetic classification through population-based incremental learning

Significant progress has been achieved in the field of Automatic Speech Recognition (ASR) thanks to the development of powerful algorithms. Research based on simplified biological models has led to the emergence of a new class called Estimation of Distribution Algorithms (EDA), which preserves significant partial solutions. Population-Based Incremental Learning (PBIL) is a technique that combines stochastic search and optimization. It is a statistical approach with evolutionary computation similar to EDAs, aimed at adapting recognition systems based on artificial Neural Networks (NN). Our main contribution lies in the improvement achieved by PBIL, which outperforms Genetic Algorithms (GA) by 16% and Evolutionary Strategies (ES) by 9.93%.

Intelligent Car Park Assist Using Fish Swarm Algorithm

In this paper a novel Intelligent Car Park Assist System is presented that uses an Evolutionary Computation method called “Fish Swarm Algorithm” or FSA, inspired from the behavior of schools of fish. The work focuses on the “parallel park” problem where a car needs to park between two already parked cars with a gap between them. The modeled car adopts the Ackermann model for moving, and has to complete the park task in 3 moves. Every move consists of steering the wheel at an angle and moving the car at a specific distance. For finding the optimal car moves to park successfully, a paradigm of the FSA algorithm is used. Simulation results confirm both the modelling soundness and the optimization capabilities of the FSA method.

An Effective Hybrid Metaheuristic Approach Based on the Genetic Algorithm

This paper presents an effective hybrid metaheuristic algorithm combining the genetic algorithm (GA) and a simple algorithm based on evolutionary computation. The evolutionary approach (EA) is applied to form the initial population of the GA, thus improving the algorithm’s performance, especially its convergence speed. To assess its effectiveness, the proposed hybrid algorithm, the EAGA, is evaluated on selected benchmark functions, as well as on a real optimisation process. The EAGA is used to identify parameters in a nonlinear system of differential equations modelling an E. coli fed-batch fermentation process. The obtained results are compared against published results from hybrid metaheuristic algorithms applied to the selected optimisation problems. The EAGA hybrid outperforms the competing algorithms due to its effective initial population generation strategy. The risk of premature convergence is reduced. Better numerical outcomes are achieved. The investigations validate the potential of the proposed hybrid metaheuristic EAGA for solving real complex nonlinear optimisation tasks.

Influence of Migration on Efficacy and Efficiency of Parallel Evolutionary Computing

Metaheuristics, such as evolutionary algorithms (EAs) have been proven to be (also theoretically, see, for example, the works of Michael Vose [1]) universal optimization methods. Previous works (Zbigniew Skolicki and Kenneth De Jong [2]) investigated impact of migration intervals on island models of EA’s in their works. Here we explore different migration intervals and amounts of migrating individuals, complementing Skolicki and DeJong’s research. In our experiments we use different ways of selecting migrants and pave the way for further research, e.g. involving different topologies and neighborhoods. We present the idea of the algorithm, show experimental results.