Swarm and Evolutionary Computation最新文献_第2页

A leader-adaptive particle swarm optimization with dimensionality reduction strategy for feature selection 用于特征选择的领导者自适应粒子群优化与降维策略

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-27 DOI: 10.1016/j.swevo.2024.101743

{"title":"A leader-adaptive particle swarm optimization with dimensionality reduction strategy for feature selection","authors":"","doi":"10.1016/j.swevo.2024.101743","DOIUrl":"10.1016/j.swevo.2024.101743","url":null,"abstract":"<div><div>Feature selection (FS) is a key data pre-processing method in machine learning tasks. It aims to obtain better classification accuracy of an algorithm with the smallest size of selected feature subset. Particle Swarm Optimization has been widely applied in FS tasks. However, when solving FS task on high-dimensional datasets, most of the PSO-based FS methods are easy to get premature convergence and fall into the local optimum. To address this issue, a leader-adaptive particle swarm optimization with dimensionality reduction strategy (LAPSO-DR) is proposed in this paper. Firstly, a hybrid initialization strategy based on feature importance is formulated. The population is divided into two parts, which have different initialization ranges. It can not only improve the diversity of the population but also eliminate some redundant features. Secondly, the leader-adaptive strategy is proposed to improve the exploitation ability of the population, in which each particle can have a different learning exemplar selected from the elite sub-swarm. Finally, the dimensionality reduction strategy based on Markov blanket is introduced to reduce the size of the optimal feature subset. LAPSO-DR is compared with 8 representative FS methods on 18 benchmark datasets. The experimental results show that LAPSO-DR can obtain smaller sizes of feature subsets with highest classification accuracies on 17 out of 18 datasets. The classification accuracies of LAPSO-DR are over 90% on 14 datasets and the feature elimination rates are higher than 60% on 18 datasets.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating logic circuit classifiers from dendritic neural model via multi-objective optimization 通过多目标优化从树突状神经模型生成逻辑电路分类器

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-26 DOI: 10.1016/j.swevo.2024.101740

{"title":"Generating logic circuit classifiers from dendritic neural model via multi-objective optimization","authors":"","doi":"10.1016/j.swevo.2024.101740","DOIUrl":"10.1016/j.swevo.2024.101740","url":null,"abstract":"<div><div>Inspired by biological neurons, a novel dendritic neural model (DNM) was proposed in our previous research to pursue a classification technique with simpler architecture, fewer parameters, and higher computation speed. The trained DNM can be transitioned to logic circuit classifiers (LCCs) by discarding unnecessary synapses and dendrites. Unlike conventional artificial neural networks with floating-point calculations, the LCC operates entirely in binary so it can be easily implemented in hardware, which has significant advantages in dealing with a high velocity of data due to its high computational speed. However, oversimplifying the model architecture will lead to the performance degeneration of LCC, and how to balance the architecture and performance is not well understood in practical applications. Therefore, the primary motivation of this study is twofold. First, a theoretical analysis is presented that the transition of LCCs from DNM can be regarded as a specific regularization problem. Second, a multiobjective optimization framework that can simultaneously optimize the classification performance and model the complexity of LCC is proposed to solve the problem. Comprehensive experiments have been conducted to validate the effectiveness and superiority of the proposed framework.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large language models as surrogate models in evolutionary algorithms: A preliminary study 进化算法中作为代用模型的大型语言模型：初步研究

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-26 DOI: 10.1016/j.swevo.2024.101741

{"title":"Large language models as surrogate models in evolutionary algorithms: A preliminary study","authors":"","doi":"10.1016/j.swevo.2024.101741","DOIUrl":"10.1016/j.swevo.2024.101741","url":null,"abstract":"<div><div>Large Language Models (LLMs) have demonstrated remarkable advancements across diverse domains, manifesting considerable capabilities in evolutionary computation, notably in generating new solutions and automating algorithm design. Surrogate-assisted selection plays a pivotal role in evolutionary algorithms (EAs), especially in addressing expensive optimization problems by reducing the number of real function evaluations. However, whether LLMs can serve as surrogate models remains an unknown. In this study, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training. Specifically, we formulate model-assisted selection as a classification problem or a regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data. This involves predicting whether a solution is good or bad, or approximating its value. This approach is then integrated into EAs, termed LLM-assisted EA (LAEA). Detailed experiments compared the visualization results of 2D data from 9 mainstream LLMs, as well as their performance on 5-10 dimensional problems. The experimental results demonstrate that LLMs have significant potential as surrogate models in evolutionary computation, achieving performance comparable to traditional surrogate models only using inference. This work offers new insights into the application of LLMs in evolutionary computation. Code is available at: <span><span>https://github.com/hhyqhh/LAEA.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization 智能交叉熵优化器：基于机器学习的新型全局优化元启发式

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-25 DOI: 10.1016/j.swevo.2024.101739

{"title":"Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization","authors":"","doi":"10.1016/j.swevo.2024.101739","DOIUrl":"10.1016/j.swevo.2024.101739","url":null,"abstract":"<div><div>Machine Learning (ML) features are extensively applied in various domains, notably in the context of Metaheuristic (MH) optimization methods. While MHs are known for their exploitation and exploration capabilities in navigating large and complex search spaces, they are not without their inherent weaknesses. These weaknesses include slow convergence rates and a struggle to strike an optimal balance between exploration and exploitation, as well as the challenge of effective knowledge extraction from complex data. To address these shortcomings, an AI-based global optimization technique is introduced, known as the Intelligent Cross-Entropy Optimizer (ICEO). This method draws inspiration from the concept of Cross Entropy (CE), a strategy that uses Kullback–Leibler or cross-entropy divergence as a measure of closeness between two sampling distributions, and it uses the potential of Machine Learning (ML) to facilitate the extraction of knowledge from the search data to learn and guide dynamically within complex search spaces. ICEO employs the Self-Organizing Map (SOM), to train and map the intricate, high-dimensional relationships within the search space onto a reduced lattice structure. This combination empowers ICEO to effectively address the weaknesses of traditional MH algorithms. To validate the effectiveness of ICEO, a rigorous evaluation involving well-established benchmark functions, including the CEC 2017 test suite, as well as real-world engineering problems have been conducted. A comprehensive statistical analysis, employing the Wilcoxon test, ranks ICEO against other prominent optimization approaches. The results demonstrate the superiority of ICEO in achieving the optimal balance between computational efficiency, precision, and reliability. In particular, it excels in enhancing convergence rates and exploration-exploitation balance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of genetic algorithms for clustering: Taxonomy and empirical analysis 聚类遗传算法调查：分类与实证分析

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-24 DOI: 10.1016/j.swevo.2024.101720

{"title":"A survey of genetic algorithms for clustering: Taxonomy and empirical analysis","authors":"","doi":"10.1016/j.swevo.2024.101720","DOIUrl":"10.1016/j.swevo.2024.101720","url":null,"abstract":"<div><div>Clustering, an unsupervised learning technique, aims to group patterns into clusters where similar patterns are grouped together, while dissimilar ones are placed in different clusters. This task can present itself as a complex optimization problem due to the extensive search space generated by all potential data partitions. Genetic Algorithms (GAs) have emerged as efficient tools for addressing this task. Consequently, significant advancements and numerous proposals have been developed in this field.</div><div>This work offers a comprehensive and critical review of state-of-the-art mono-objective Genetic Algorithms (GAs) for partitional clustering. From a more theoretical standpoint, it examines 22 well-known proposals in detail, covering their encoding strategies, objective functions, genetic operators, local search methods, and parent selection strategies. Based on this information, a specific taxonomy is proposed. In addition, from a more practical standpoint, a detailed experimental study is conducted to discern the advantages and disadvantages of approaches. Specifically, 22 different cluster validation indices are considered to compare the performance of clustering techniques. This evaluation is performed across 94 datasets encompassing diverse configurations, including the number of classes, separation between classes, and pattern dimensionality. Results reveal interesting findings, such as the key role of local search in optimizing results and reducing search space. Additionally, representations based on centroids and labels demonstrate greater efficiency and crossover and mutation operators do not prove to be as relevant. Ultimately, while the results are satisfactory, real-world clustering problems introduce additional complexity, especially for algorithms aiming to determine the number of clusters, resulting in diminished performance and the need for new approaches to be explored. Code, datasets and instructions to run algorithms in the LEAL library are available in an associated repository, in order to facilitate future experiments in this environment.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning assisted novelty search in Voronoi regions for constrained multi-objective optimization 针对受限多目标优化的沃罗诺伊区域深度强化学习辅助新奇搜索

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-24 DOI: 10.1016/j.swevo.2024.101732

{"title":"Deep reinforcement learning assisted novelty search in Voronoi regions for constrained multi-objective optimization","authors":"","doi":"10.1016/j.swevo.2024.101732","DOIUrl":"10.1016/j.swevo.2024.101732","url":null,"abstract":"<div><div>Solving constrained multi-objective optimization problems (CMOPs) requires optimizing multiple conflicting objectives while satisfying various constraints. Existing constrained multi-objective evolutionary algorithms (CMOEAs) cross infeasible regions by ignoring constraints. However, these methods might neglect promising search directions, leading to insufficient exploration of the search space. To address this issue, this paper proposes a deep reinforcement learning assisted constrained multi-objective quality-diversity algorithm. The proposed algorithm designs a diversity maintenance mechanism to promote evenly coverage of the final solution set on the constrained Pareto front. Specifically, first, a novelty-oriented archive is created using a centroid Voronoi tessellation, which divides the search space into a desired number of Voronoi regions. Each region acts as a repository of non-dominated solutions with different phenotypic characteristics to provide diversity information and supplementary evolutionary trails. Secondly, to improve resource utilization, a deep Q-network is adopted to learn a policy to select suitable Voronoi regions for offspring generation based on their novelty scores. The exploration of these regions aims to find a set of diverse, high-performing solutions to accelerate convergence and escape local optima. Compared with eight state-of-the-art CMOEAs, experimental studies on four benchmark suites and nine real-world applications demonstrate that the proposed algorithm exhibits superior or at least competitive performance, especially on problems with discrete and narrow feasible regions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics 利用超参数调整元搜索法识别工业活性污泥模型

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-20 DOI: 10.1016/j.swevo.2024.101733

引用次数: 0

Constrained large-scale multiobjective optimization based on a competitive and cooperative swarm optimizer 基于竞争与合作蜂群优化器的有约束大规模多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-20 DOI: 10.1016/j.swevo.2024.101735

{"title":"Constrained large-scale multiobjective optimization based on a competitive and cooperative swarm optimizer","authors":"","doi":"10.1016/j.swevo.2024.101735","DOIUrl":"10.1016/j.swevo.2024.101735","url":null,"abstract":"<div><p>Many engineering application problems can be modeled as constrained multiobjective optimization problems (CMOPs), which have attracted much attention. In solving CMOPs, existing algorithms encounter difficulties in balancing conflicting objectives and constraints. Worse still, the performance of the algorithms deteriorates drastically when the size of the decision variables scales up. To address these issues, this study proposes a competitive and cooperative swarm optimizer for large-scale CMOPs. To balance conflict objectives and constraints, a bidirectional search mechanism based on competitive and cooperative swarms is designed. It involves two swarms, approximating the true Pareto front from two directions. To enhance the search efficiency in large-scale space, we propose a fast-converging competitive swarm optimizer. Unlike existing competitive swarm optimizers, the proposed optimizer updates the velocity and position of all particles at each iteration. Additionally, to reduce the search range of the decision space, a fuzzy decision variables operator is used. Comparison experiments have been performed on test instances with 100–1000 decision variables. Experiments demonstrate the superior performance of the proposed algorithm over five peer algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving multi-objective robust optimization problems via Stakelberg-based game model 通过基于 Stakelberg 的博弈模型解决多目标鲁棒优化问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-18 DOI: 10.1016/j.swevo.2024.101734

{"title":"Solving multi-objective robust optimization problems via Stakelberg-based game model","authors":"","doi":"10.1016/j.swevo.2024.101734","DOIUrl":"10.1016/j.swevo.2024.101734","url":null,"abstract":"<div><p>Real-world multi-objective engineering problems frequently involve uncertainties stemming from environmental factors, production inaccuracies, and other sources. A critical aspect of addressing these problems, termed Multi-Objective Robust Optimization (MORO) problems, is the development of solutions that are both optimal and resilient to uncertainties. This paper proposes addressing these uncertainties through the application of Stackelberg game models, a novel approach involving the interaction of two players. The Leader searches for optimal and robust solutions and the Follower generates uncertainties based on the Leader’s chosen solutions. The Follower seeks to tackle the most challenging uncertainties associated with the Leader’s candidate solutions. Additionally, this paper introduces a novel metric to assess the robustness of a given set of solutions concerning specified uncertainties.</p><p>Based on the proposed approach, a co-evolutionary algorithm is developed. A numerical study is then conducted to evaluate the algorithm by comparing its performance with those obtained by four benchmark algorithms on nine benchmark MORO problems. The numerical study also aims to assess its sensitivity to run parameter variations. The experimental results demonstrate the proposed approach’s effectiveness in identifying a non-dominated robust set of solutions.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SaDENAS: A self-adaptive differential evolution algorithm for neural architecture search SaDENAS：用于神经架构搜索的自适应差分进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-18 DOI: 10.1016/j.swevo.2024.101736

{"title":"SaDENAS: A self-adaptive differential evolution algorithm for neural architecture search","authors":"","doi":"10.1016/j.swevo.2024.101736","DOIUrl":"10.1016/j.swevo.2024.101736","url":null,"abstract":"<div><p>Evolutionary neural architecture search (ENAS) and differentiable architecture search (DARTS) are all prominent algorithms in neural architecture search, enabling the automated design of deep neural networks. To leverage the strengths of both methods, there exists a framework called continuous ENAS, which alternates between using gradient descent to optimize the supernet and employing evolutionary algorithms to optimize the architectural encodings. However, in continuous ENAS, there exists a premature convergence issue accompanied by the small model trap, which is a common issue in NAS. To address this issue, this paper proposes a self-adaptive differential evolution algorithm for neural architecture search (SaDENAS), which can reduce the interference caused by small models to other individuals during the optimization process, thereby avoiding premature convergence. Specifically, SaDENAS treats architectures within the search space as architectural encodings, leveraging vector differences between encodings as the basis for evolutionary operators. To achieve a trade-off between exploration and exploitation, we integrate both local and global search strategies with a mutation scaling factor to adaptively balance these two strategies. Empirical findings demonstrate that our proposed algorithm achieves better performance with superior convergence compared to other algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0