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On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem

evolutionary COMPUTATION 2024年第2期32卷 143-175页

作者： Bossek, Jakob Grimme, Christian Rhein Westfal TH Aachen Dept Comp Sci AI Methodol Aachen Germany Univ Munster Dept Informat Syst Stat & Optimizat Munster Germany

We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyze the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a *** prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph-based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.

关键词： evolutionary algorithms multiobjective optimization combinatorial optimization minimum spanning tree problem biased mutation

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Multiple landscape measure-based approach for dynamic optimization problems

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SWARM AND evolutionary COMPUTATION 2024年 88卷

作者： Li, Kangjing Elsayed, Saber Sarker, Ruhul Essam, Daryl Univ New South Wales Sch Syst & Comp Northcott Dr Canberra ACT 2600 Australia

Many practical decision -making problems involve dynamic scenarios, where the decision variables, conditions and/or parameters of their optimization models change over time. Such problems are known as dynamic optimization problems (DOPs). Although evolutionary algorithms (EAs) have been effective in solving static optimization problems, they face challenges in handling DOPs. To improve the performance of EAs in dealing with DOPs, this paper proposes a new evolutionary framework that uses different landscape measures to analyze problem landscapes and utilizes the information gained from this to improve the searching process. The proposed method is adopted in three EAs to deal with dynamic functions from IEEE-CEC2009 and two real -world problems. According to the experimental results, LIDOA with multi -measure methods improves the performance of GA, jDE and CMA-ES, on average by 6.71%, 3.03% and 7.78% on benchmark problems, respectively.

关键词： Dynamic optimization problems evolutionary algorithms Fitness landscape analysis

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Parallel Computing Utilization in Nonlinear Model Predictive Control of Permanent Magnet Synchronous Motor

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IEEE ACCESS 2024年 12卷 128187-128200页

作者： Kozubik, Michal Vesely, Libor Aufderheide, Eyke Vaclavek, Pavel Brno Univ Technol Cent European Inst Technol CEITEC Brno 61200 Czech Republic Brno Univ Technol Dept Control & Instrumentat Brno 61600 Czech Republic Tech Univ Munich Chair Elect Dr Syst & Power Elect D-80333 Munich Germany

Permanent Magnet Synchronous Motor (PMSM) drives are widely used for motion control industrial applications and electrical vehicle powertrains, where they provide a good torque-to-weight ratio and a high dynamical performance. With the increasing usage of these machines, the demands on exploiting their abilities are also growing. Usual control techniques, such as field-oriented control (FOC), need some workaround to achieve the requested behavior, e.g., field-weakening, while keeping the constraints on the stator currents. Similarly, when applying the linear model predictive control, the linearization of the torque function and defined constraints lead to a loss of essential information and sub-optimal performance. That is the reason why the application of nonlinear theory is necessary. Nonlinear Model Predictive Control (NMPC) is a promising alternative to linear control methods. However, this approach has a major drawback in its computational demands. This paper presents a novel approach to the implementation of PMSMs' NMPC. The proposed controller utilizes the native parallelism of population-based optimization methods and the supreme performance of field-programmable gate arrays to solve the nonlinear optimization problem in the time necessary for proper motor control. The paper presents the verification of the algorithm's behavior both in simulation and laboratory experiments. The proposed controller's behavior is compared to the standard control technique of FOC and linear MPC. The achieved results prove the superior quality of control performed by NMPC in comparison with FOC and LMPC. The controller was able to follow the Maximal Torque Per Ampere strategy without any supplementary algorithm, altogether with constraint handling.

关键词： Torque Parallel processing Predictive control Optimization Permanent magnet motors Vectors Stators evolutionary algorithms motor control nonlinear control parallel computing predictive control

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Directional cooperative networks

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NEUROCOMPUTING 2024年 593卷

作者： Ito, Yasuaki Nguyen, Le-Minh Japan Adv Inst Sci & Technol Grad Sch Informat Sci 1-1 Asahidai Nomi Ishikawa 9231292 Japan

Although various machine learning methods have been proposed for industrial applications, there are not many examples of their application in some industrial sectors. Data collected within organizations are inconsistently formatted and expensive to annotate, resulting in insufficient datasets for training. This hinders the widespread use of machine learning methods. The challenge is to address the need for more performance of machine learning models due to the lack of available data. We address this problem by introducing Directed Cooperative Networks (DCNs). This approach addresses the lack of data by connecting two neural networks with a function that evaluates the output of Generator. Estimator, the one of the networks, acts as an approximate function of the evaluation function, assisting Generator to output a product that yields the desired evaluation function value. When the networks are sufficiently trained, Estimator's output approaches the output of the evaluation function, and Generator will produce products with the desired attributes. A data -independent method is the evolutionary algorithm (EA). Since EA is optimized by feedback from the environment, good results can be obtained by using the evaluation function of the product as its environment. However, its effectiveness decreases as the combination of product components becomes more complex. One of the outstanding properties of DCN is its potential to outperform EA on complex problems because it learns using the gradient of the search field. DCN does not require rewriting the evaluation function into a backpropagatable form, even if the function is complex. Using a neural network that behaves as an approximate function of the evaluation function, the gradient descent method can be applied even if the evaluation function is non -differentiable. To validate the effectiveness of the proposed method, DCN is applied to a molecular search task and analyzed in comparison with other approaches. This study aims

关键词： Unsupervised learning Regression model Neural networks evolutionary algorithms

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DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution

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SOFTWAREX 2025年 29卷

作者： Zielinski, Blazej Sciegienny, Szymon Orlicki, Hubert Ksiazek, Wojciech Cracow Univ Technol Fac Comp Sci & Telecommun Dept Comp Sci Krakow Poland

The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.

关键词： evolutionary computation Genetic algorithm Differential evolution Optimization evolutionary algorithms Metaheuristics

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Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing

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SWARM AND evolutionary COMPUTATION 2024年 91卷

作者： Zheng, Yu-Jun Xie, Xi-Cheng Zhang, Zhi-Yuan Shi, Jin-Tang Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

Icy rainfall and snowfall in 2024 Spring Festival struck the high-speed railway catenary systems and caused serious traffic disruptions in central and eastern China. Deicing drones are an effective method in response to these freezing events due to their fast speed and high environmental tolerance. However, the large disaster- affected area and the large scale and complexity of catenary networks make deicing drone scheduling a very difficult problem. In this paper, we formulate two versions of deicing drone scheduling problem, one for single drone scheduling and the other for multiple drone scheduling. Unlike most existing vehicle/drone routing problems, our problem aims to minimize the total negative effect caused by the freezing events on train operations, which reflects the prime concern of the decision-maker and is highly complex. To efficiently solve the problem, we propose a reinforcement learning assisted memetic optimization algorithm, which integrates global mutation and a set of neighborhood search operators adaptively selected by deep reinforcement learning. Computational results on real-world problem instances demonstrate its significant performance advantages over selected popular optimization algorithms in the literature.

关键词： Drone scheduling Catenary deicing Memetic optimization evolutionary algorithms Neighborhood search Deep reinforcement learning

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A systematic literature review on meta-heuristic based feature selection techniques for text classification

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2084页

作者： Al-shalif, Sarah Abdulkarem Senan, Norhalina Saeed, Faisal Ghaban, Wad Ibrahim, Noraini Aamir, Muhammad Sharif, Wareesa Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Parit Raja Johor Malaysia Univ Birmingham Sch Comp & Digital Technol Dept Comp & Data Sci DAAI Res Grp Birmingham England Univ Tabuk Appl Coll Tabuk Saudi Arabia Univ Derby Sch Elect Comp & Math Derby England Islamia Univ Bahawalpur Fac Comp Bahawalpur Pakistan

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

关键词： Feature selection Meta-heuristic techniques Text classi fi cation Dimensionally reduction evolutionary algorithms Ringed seal search Ant colony optimization Particle swarm optimization Genetic algorithm Learning accuracy

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Boosting scalability for large-scale multiobjective optimization via transfer weights

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INFORMATION SCIENCES 2024年 670卷

作者： Hong, Haokai Jiang, Min Yen, Gary G. Xiamen Univ Sch Informat Dept Artificial Intelligence Minist Culture & TourismKey Lab Digital Protect & 422 South Siming Rd Xiamen 361005 Fujian Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74074 USA

Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple conflicting objectives with hundreds or even thousands of decision variables, demand increasing computational resources to assure satisfactory performance as the decision variables increase. Multiobjective evolutionary algorithms are naturally scalable, but as the dimension increases, the conflict between analyzing enough solutions and the limited number of function evaluations has hindered further improvements in scalability. In this paper, we first define the scalability of multiobjective evolutionary algorithms and design an indicator to quantitatively measure the scalability. Second, to boost scalability when solving LSMOPs, we propose a scalable multiobjective optimization algorithm by transferring weights between solutions to reduce dependency on ever-increasing computational resources as the problem dimension increases. The proposed framework entails constructing a latent decision space to determine evolutionary weights for chosen representative solutions. These computed weights are then transferred to the remaining solutions, enabling evolutionary optimization to proceed without requiring additional evaluations, even as the dimensionality increases. By utilizing the knowledge gained from the source solutions, each solution is customized with an evolutionary weight scheme that not only preserves computational resources but also enhances optimization performance, thereby boosting scalability. We have conducted experiments on LSMOPs to verify the effectiveness and scalability of the proposed algorithm. The proposed method outperforms selected state-of-the-art algorithms and gains scalability boosts in situations where the dimensions increase while the function evaluation remains constant.

关键词： evolutionary algorithms Large-scale optimization Multiobjective optimization Transfer learning

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On reconfiguring heterogeneous parallel island models

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SWARM AND evolutionary COMPUTATION 2024年 89卷

作者： da Silveira, Lucas A. de Lima, Thaynara A. Ayala-Rincon, Mauricio Univ Brasilia Dept Comp Sci BR-70900010 Brasilia DF Brazil Univ Fed Goias Inst Math & Stat Campus II BR-74001970 Goiania GO Brazil Univ Brasilia Dept Math BR-70900010 Brasilia DF Brazil

This work introduces a new Parallel Island Model (PIM) that encompasses the benefits of heterogeneity and algorithmic reconfigurability. The former feature, heterogeneity, means that different islands may execute different evolutionary algorithms. The latter, reconfigurability, means that each island can change the algorithm being executed during the evolutionary process. Allowing such features increases the usual diversity obtained by the communication topologies and migration policies by homogeneous PIMs (HoPIMs). Previous (non-reconfigurable) heterogeneous PIMs (HePIMs) were able to provide competitive solutions regarding the HoPIMs. By adding the reconfiguration capability, PIMs can change dynamically from executing one evolutionary algorithm to another. In this manner, the required diversity and flexibility to outperform HoPIMs and HePIMs is achieved. This paper discusses policies to profit from the feature of reconfigurability on HePIM models and provides an innovative and successful stagnation -based reconfiguration policy. The benefits of the new reconfigurable model are verified using the unsigned reversal distance optimization problem as a case study.

关键词： Island-based bioinspired algorithms Parallel island models Heterogeneous models Reconfigurability evolutionary algorithms Unsigned reversal distance

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An Improved Hypervolume-Based evolutionary Algorithm for Many-Objective Optimization 8th

An Improved Hypervolume-Based Evolutionary Algorithm for Ma...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Wen, Chengxin Li, Lihua Ma, Hongbin School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

The hypervolume indicator is commonly utilized in indicator-based evolutionary algorithms due to its strict adherence to the Pareto domination relationship. However, its high computational complexity in high-dimensional objective spaces limits its widespread adoption and application. In this paper, we propose a fast and efficient method for approximating the overall hypervolume to overcome this challenge. We then integrate this method into the basic evolutionary computation framework, forming an algorithm for solving many-objective optimization problems. To evaluate its performance, we compared our proposed algorithm with six state-of-the-art algorithms on WFG and DTLZ test problems with 3, 5, 10, and 15 objectives. The results demonstrate that our proposed method is highly competitive in most cases. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： evolutionary algorithms

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