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检索条件"任意字段=5th European Conference on Evolutionary Computation in Combinatorial Optimization"

共 268 条记录，以下是31-40 订阅

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Injecting combinatorial optimization into MCTS: Application to the Board Game Boop

Injecting Combinatorial Optimization into MCTS: Application ...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Richoux, Florian AIST Tokyo Japan

ISBN: (纸本)9783031900648

Games, including abstract board games, constitute a convenient ground to create, design, and improve new AI methods. In this field, Monte Carlo Tree Search is a popular algorithm family, aiming to build game trees and explore them efficiently. combinatorial optimization, on the other hand, aims to model and solve problems with an objective to optimize and constraints to satisfy, and is less common in Game AI. We believe however that both methods can be combined efficiently, by injecting combinatorial optimization into Monte Carlo Tree Search to help the tree search, leading to a novel combination of these two techniques. Tested on the board game boop., our method beats 96% of the time the Monte Carlo Tree Search algorithm baseline. We conducted an ablation study to isolate and analyze which injections and combinations of injections lead to such performances. Finally, we opposed our AI method against human players on the Board Game Arena platform, and reached a 373 ELO rating after 51 boop. games, with a 69% win rate and finishing ranked 56th worldwide on the platform over 5,316 boop. players. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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Probing LLMs on optimization Problems: Can they Recall and Interpret Problem Features?

Probing LLMs on Optimization Problems: Can They Recall and...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Da Ros, Francesca Di Gaspero, Luca Roitero, Kevin University of Udine Udine Italy

ISBN: (纸本)9783031900648

In this study, we explore the ability of Large Language Models (LLMs) to understand and recall features associated with combinatorial optimization problems in both Natural Language Processing (NLP) and structured contexts. By probing LLMs using a diverse set of optimization problem instances, we aim to evaluate the models’ ability to accurately extract and reason about key attributes, such as parameters and features. Our methodology involves both structured and extended NLP-based prompts for the models and instructing these models to identify specific features from the provided problem instances. the results reveal that while LLMs exhibit some capacity to identify and extract information, they fail to recall 100% of even the simplest features consistently present within the text. this limitation underscores the current challenges LLMs face in precise reasoning and feature extraction tasks, suggesting the need for further refinement in their interpretability and understanding capabilities when applied to structured problem-solving domains. (Relevant data and code are available at the following link: https://***/fw6ta/?view_only=d8e63cdda6bd409b83aa3d9a4b025b06). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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An Investigation of Structural Bias in Particle Swarm optimization

An Investigation of Structural Bias in Particle Swarm Opti...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Ibehej, David Kůdela, Jakub Institute of Automation and Computer Science Brno University of Technology Technicka 2 Brno621 00 Czech Republic

ISBN: (纸本)9783031900648

Many complex optimization problems, for which standard methods do not provide good-enough solutions, require the utilization of efficient metaheuristics. However, it has been found that several metaheuristics suffer from different types of structural biases, that may undermine their performance. In this paper, we investigate one of the most used evolutionary computation methods, the Particle Swarm optimization algorithm. We utilize a recently developed modular framework for the construction of 123,900 parameterizations of PSO and investigate the effect of the different parameter choices on the identified types of structural bias. We also analyze and discuss the impact of the different types of structural bias found in these parameterizations on their performance on functions from a standard test set. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Geometric Particle Swarm optimization in Program Trace optimization

Geometric Particle Swarm Optimization in Program Trace Opti...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Moraglio, Alberto McDermott, James University of Exeter Exeter United Kingdom University of Galway Galway Ireland

ISBN: (纸本)9783031900648

On the 30th anniversary of Particle Swarm optimization (PSO), we present a novel integration of Geometric PSO (GPSO) into the Program Trace optimization (PTO) framework. GPSO extends PSO to diverse representations in a principled manner, while PTO provides automatic representation design for arbitrary problem structures. By specializing GPSO to PTO’s trace representation, we achieve a universal PSO variant applicable out-of-the-box to any representation and problem. We detail the theoretical foundations of this integration and evaluate our approach on diverse optimization problems across multiple representations. Our work demonstrates the power of combining geometric generalizations with automatic representation design, here specifically obtaining a truly general form of PSO that applies to any representation while retaining its original essence. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Proposal of Efficient Particle Swarm optimization for Constrained optimization Problems

Proposal of Efficient Particle Swarm Optimization for Constr...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fukuhara, So Arakawa, Masao Kagawa University 1-1 Saiwaicho Kagawa Takamatsu Japan 2-7 Hibikino Wakamatsu Ward Fukuoka Kitakyusyu Japan

ISBN: (纸本)9783031900648

Engineering design problems often involve various requirements and complexities, which are treated as constraints in optimization problems. Constrained optimization problems (COPs) are widely studied, and particle swarm optimization (PSO) has become a popular method for solving them due to its simplicity and effective convergence. However, PSOs still face limitations, particularly in terms of search efficiency and high computation costs for optimal solutions in complex feasible areas or near constraint boundaries. this paper presents a novel particle swarm optimization algorithm named independent 2-group particle swarm optimization (I2GPSO). I2GPSO is based on the following ideas - a constraint handling method, a novel structure of particles and a novel local search operator. the constraint handling method uses the existing penalty function method. the structure of particles defines two particle groups that have original roles, efficiently enabling PSO to search globally and locally. the local search operator is introduced into one group and enables particles to intensively search near the good solutions obtained by particles so far. these novel approaches effectively reinforce the optimization efficiency of the PSO algorithm. the optimization capability and characteristics of I2GPSO is illustrated in engineering design problems. the results are compared with other state-of-the-art PSOs, and it is shown that the proposed algorithm possesses competitive search efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Fair Ambulance Allocation via Multi-Objective evolutionary optimization

Fair Ambulance Allocation via Multi-Objective Evolutionary ...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Åmellem Kallekleiv, Torjus Mengshoel, Ole Jakob Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783031900648

the first contact between patients and the healthcare system often occurs through prehospital care, administered by emergency medical services (EMS). However, these services are inherently costly, and costs increase significantly with the number of ambulances, staff, and depots (or stations). Further, achieving fairness goals set for EMS response times remains critical. these goals typically vary between different types of incidents (acute versus urgent) and different types of regions of a country (rural versus urban). this study formulates fairness as explicit objectives within a multi-objective optimization framework. We leverage single- and multi-objective metaheuristic optimization algorithms to identify efficient and fair ambulance allocation strategies. Our empirical study, which is based on an incident data set provided by the Emergency Medical Communication Center (EMCC) of Oslo University Hospital (OUH) and the Norwegian National Advisory Unit for Prehospital Emergency Medicine (NAKOS), demonstrates the benefit of multi-objective formulation relative to the alternatives. On high-demand days, multi-objective adaptive ambulance allocations reduce response time violations by up to 31.4%. Key contributions include: (1) Formulating ambulance allocation as a multi-objective problem balancing fairness and efficiency;(2) Demonstrating the benefits of time segmentation, where reallocations at optimal intervals significantly improve compliance rates;and (3) Showing that NSGA-II consistently outperforms a traditional genetic algorithm in minimizing response time violations, particularly under high-demand scenarios. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multiobjective optimization

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A Survey of Modern Hybrid Particle Swarm optimization Algorithms

A Survey of Modern Hybrid Particle Swarm Optimization Algor...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Grazioso, Matteo Gallese, Chiara Vanneschi, Leonardo Nobile, Marco S. Ca’ Foscari University Venice Italy University of Turin Turin Italy Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

ISBN: (纸本)9783031900648

Bio-inspired, population-based meta-heuristic for global optimization are very popular algorithms for addressing complex computational problems that traditional methods struggle to solve. Among the existing algorithms, the swarm intelligence algorithm Particle Swarm optimization (PSO) is one of the most popular, thanks to its simplicity and effectiveness in multiple scenarios. this article focuses on recent hybrid optimization methods that extend the basic functioning of PSO. Hybridization, in this context, is defined as the integration of PSO with a different technique, to take advantage of the strengths of both algorithms. According to our findings, many variants have been proposed. the most frequent solutions consist of the hybridization of PSO with evolutionary operators (e.g. Genetic Algorithms and Differential Evolution);such strategies usually maintain a high degree of diversity into the population, enhancing global search capability, while reducing the risk of stagnation. Meanwhile the most widespread applications are from the areas of energy optimization, structural engineering and machine learning problems, demonstrating the versatility of these hybrid approaches. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Analyzing the Effects of Memetic Variations on Convergence in Overlapping Swarm Intelligence

Analyzing the Effects of Memetic Variations on Convergenc...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Patera, Nathan Sheppard, John Gianforte School of Computing Montana State University BozemanMT59717 United States

ISBN: (纸本)9783031900648

evolutionary algorithms often struggle when there exists a high degree of interdependence between the variables (epistasis), non-separability, discontinuity, high dimensionality, and sparsity. these complexities can lead to hitchhiking, where poor parameters are associated with good schemata, and "two steps forward and one step back" where near-optimal parameters are lost in favor of lower-quality parameters that immediately improve fitness;phenomena that contribute to premature convergence. Overlapping Swarm Intelligence (OSI) has been introduced as a cooperative coevolutionary algorithm that utilizes overlap of variables between subswarms to encourage sharing and competing among variables across the subswarms. OSI has shown success in handling epistasis, however it can still suffer from premature convergence when subswarms get stuck in "pseudo-optima," i.e., when a subset of variables is at a minimum with respect to the reduced search space but not the entire space. We investigate convergence on memetic variations of OSI, CPSO (OSI with no overlap), and Particle Swarm optimization using block coordinate descent applied to the CEC2010 Benchmark problems. the memetic algorithms show some success in assisting subpopulations in finding more optimal solutions compared to their non-memetic counterparts. We also use the Gini coefficient on a solution’s derivative to estimate the proportion of variables stuck in pseudo-optima to help understand what contributes to premature convergence. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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A PSO-Based MPPT with Dynamic Monitoring Reset for PV Systems

A PSO-Based MPPT with Dynamic Monitoring Reset for PV Syst...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： da Rosa, Igor de Matos Panisson, Alison R. Schmitz, Lenon Department of Computing Federal University of Santa Catarina Santa Catarina Brazil

ISBN: (纸本)9783031900648

Photovoltaic (PV) systems have become essential in the transition to sustainable energy solutions. However, these systems face challenges in maximizing energy extraction, particularly under partial shading conditions. this study introduces an advanced Maximum Power Point Tracking (MPPT) approach that combines the Particle Swarm optimization (PSO) algorithm with a Dynamic Monitoring Reset (DMR) mechanism. the DMR enhances the PSO’s ability to adapt and maintain tracking accuracy in rapidly changing shading environments, providing robust tracking of the Global Maximum Power Point (GMPP). Simulation results demonstrate that the PSO-DMR method significantly improves PV system efficiency, reducing energy losses and optimizing performance under variable and complex shading patterns. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Search Trajectory Networks Applied to a Real-World Parallel Batch Scheduling Problem

Search Trajectory Networks Applied to a Real-World Paralle...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Da Ros, Francesca Di Gaspero, Luca Lackner, Marie-Louise Musliu, Nysret Soprano, Michael Università degli Studi di Udine Udine Italy Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling TU Wien Vienna Austria

ISBN: (纸本)9783031900617

We investigate solution methods for the Oven Scheduling Problem (OSP), a parallel batch scheduling optimization problem in semiconductor manufacturing, using Search Trajectory Networks (STNs). STNs are a recently introduced tool to analyze and compare the behavior of metaheuristic algorithms concerning their exploration ability w.r.t. single problem instances. We consider two state-of-the-art algorithms for the OSP, a Simulated Annealing (SA) and a Large Neighborhood Search (LNS), and instances from the literature. the STNs enable us to draw the following conclusions: (i) the two algorithms’ trajectories overlap especially at the beginning of the trajectories, as revealed by a search space partitioning based on Hierarchical Agglomerative Clustering;(ii) the fitness landscape of many instances is multi-modal, with several high-quality solutions scattered in the search space;(iii) SA trajectories are longer, but the number of locations visited by SA and LNS is similar. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Simulated annealing

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