Browsing by Keyword "Metaheuristics"

Now showing 1 - 8 of 8
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    Combining physics-based and data-driven methods in metal stamping
    (2024) Abanda, Amaia; Arroyo, Amaia; Boto, Fernando; Esteras, Miguel; IA; PROMETAL; FACTORY
    This work presents a methodology for combining physical modeling strategies (FEM), machine learning techniques, and evolutionary algorithms for a metal stamping process to ensure process quality during production. Firstly, a surrogate model or metamodel is proposed to approximate the behavior of the simulation model for different outputs in a fraction of time. Secondly, based on the surrogate model, multiple soft sensors that estimate different quality measures of the stamped part departing from the draw-ins are proposed, which enables their integration into the process. Lastly, evolutionary algorithms are used to estimate the latent blank characteristics and for the prescriptions of process parameters that maximize the quality of the stamped part. The obtained numerical results are promising, with relative errors around 2 2% in most cases and outperforming a naive method. This methodology aims to be a decision support system that moves towards zero defects in the stamping process from the process conception phase.
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    Editorial: Memetic Computing: Accelerating optimization heuristics with problem-dependent local search methods
    (2022-04) Osaba, Eneko; Del Ser, Javier; Cotta, Carlos; Moscato, Pablo; Quantum; IA
    Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-based methods or any other specialized procedures that encompass problem-specific knowledge in a variety of real-world scenarios. From the perspective of the algorithms themselves, this hybridization can be realized in many different ways: it is this upsurge of manifold algorithmic approaches what has maintained a vigorous and intense activity around Memetic Computing over the years, progressively adapting the paradigm to newly emerging problem formulations and characteristics. This editorial introduces the readership of Swarm and Evolutionary Computation to the contributions finally included in the Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods. The high quality of the works presented in this editorial unquestionably proves the excellent health of this vibrant research area, as well as its continued success at tackling challenging real-world optimization problems.
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    Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives: A Critical Survey, Results, and Perspectives
    (2022-01-29) Para, Jesus; Del Ser, Javier; Nebro, Antonio J.; IA
    In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.
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    jMetalPy: A Python framework for multi-objective optimization with metaheuristics: A Python framework for multi-objective optimization with metaheuristics
    (2019-12) Benítez-Hidalgo, Antonio; Nebro, Antonio J.; García-Nieto, José; Oregi, Izaskun; Del Ser, Javier; Quantum; IA
    This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.
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    Let nature decide its nature: On the design of collaborative hyperheuristics for decentralized ephemeral environments: On the design of collaborative hyperheuristics for decentralized ephemeral environments
    (2018-11) Martinez, Aritz; Osaba, Eneko; Bilbao, Miren Nekane; Ser, Javier Del; Quantum; IA
    The research community has traditionally aimed at the derivation and development of metaheuristic solvers, suited to deal with problems of very diverse characteristics. Unfortunately, it is often the case that new metaheuristic techniques are presented and assessed in a reduced set of cases, mostly due to the lack of computational resources to undertake extensive performance studies over a sufficiently diverse set of optimization benchmarks. This manuscript explores how ephemeral environments could be exploited to efficiently construct metaheuristic algorithms by virtue of a collaborative, distributed nature-inspired hyperheuristic framework specifically designed to be deployed over unreliable, uncoordinated computation nodes. To this end, the designed framework defines two types of nodes (trackers and peers, similarly to peer-to-peer networks), both reacting resiliently to unexpected disconnections of nodes disregarding their type. Peer nodes exchange their populations (i.e. constructed algorithms) asynchronously, so that local optima are avoided at every peer thanks to the contribution by other nodes. Furthermore, the overall platform is fully scalable, allowing its users to implement and share newly derived operators and fitness functions so as to enrich the diversity and universality of the heuristic algorithms found by the framework. Results obtained from in-lab experiments with a reduced number of nodes are discussed to shed light on the evolution of the best solution of the framework with the number of connected peers and the tolerance of the network to node disconnections.
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    A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problems
    (Springer Science and Business Media Deutschland GmbH, 2021-11-23) Osaba, Eneko; Loizaga, Erlantz; Goenaga, Xabier; Sanchez, Valentin; Camacho, David; Tino, Peter; Allmendinger, Richard; Yin, Hujun; Tallón-Ballesteros, Antonio J.; Tang, Ke; Cho, Sung-Bae; Novais, Paulo; Nascimento, Susana; Quantum; ADV_INTER_PLAT; HPA
    In recent years, industry has evolved towards the efficient digitalization and optimization of products and processes. This situation is the consequence of the huge amount of information available in indus trial environments and its efficient management for reaching unprece dented productivity levels. The momentum that enjoys this application field has led to the proposal of advanced methods for the dealing of robotic processes in industrial plants, optimal packaging of goods and the efficient scheduling of production plans, among many others. This paper is focused on the last of these categories. More concretely, we present a Parallel Variable Neighborhood Search for solving an industrial problem in which a fixed amount of materials should be constructed into a limited number of production lines. The construction of these materials has sev eral particularities, such as the need of some specific tools to be correctly produced. It is also relevant to underscore that the problem solved in this research corresponds to a real-world situation, and that it is currently deployed in a production plant in the Basque Country (Spain).
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    Soft Computing for Swarm Robotics: New Trends and Applications
    (2020-01) Osaba, Eneko; Del Ser, Javier; Iglesias, Andres; Yang, Xin She; Quantum; IA
    Robotics have experienced a meteoric growth over the last decades, reaching unprecedented levels of distributed intelligence and self-autonomy. Today, a myriad of real-world scenarios can benefit from the application of robots, such as structural health monitoring, complex manufacturing, efficient logistics or disaster management. Related to this topic, there is a paradigm connected to Swarm Intelligence which is grasping significant interest from the Computational Intelligence community. This branch of knowledge is known as Swarm Robotics, which refers to the development of tools and techniques to ease the coordination of multiple small-sized robots towards the accomplishment of difficult tasks or missions in a collaborative fashion. The success of Swarm Robotics applications comes from the efficient use of smart sensing, communication and organization functionalities endowed to these small robots, which allow for collaborative information sensing, operation and knowledge inference from the environment. The numerous industrial and social applications that can be addressed efficiently by virtue of swarm robotics unleashes a vibrant research area focused on distributing intelligence among autonomous agents with simple behavioral rules and communication schedules, yet potentially capable of realizing the most complex tasks. In this context, we present and overview recent contributions reported around this paradigm, which serves as an exemplary excerpt of the potential of Swarm Robotics to become a major research catalyst of the Computational Intelligence arena in years to come.
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    A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems
    (2021-07) Osaba, Eneko; Villar-Rodriguez, Esther; Del Ser, Javier; Nebro, Antonio J.; Molina, Daniel; LaTorre, Antonio; Suganthan, Ponnuthurai N.; Coello Coello, Carlos A.; Herrera, Francisco; Quantum; IA
    In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.