Artykuły w czasopismach: „METAHEURISTIC APPROACH”

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Laudis, Lalin L., and Amit Kumar Sinha. "Metaheuristic Approach for VLSI 3D-Floorplanning." International Journal of Scientific Research 2, no. 12 (2012): 202–3. http://dx.doi.org/10.15373/22778179/dec2013/62.

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LEE, YOUNG CHOON, JAVID TAHERI, and ALBERT Y. ZOMAYA. "A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS." International Journal of Foundations of Computer Science 23, no. 02 (2012): 445–64. http://dx.doi.org/10.1142/s0129054112400229.

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A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.

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Bajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby, and James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation." Electronics 12, no. 12 (2023): 2661. http://dx.doi.org/10.3390/electronics12122661.

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We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.

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Rosłon, Jerzy Hubert, and Janusz Edward Kulejewski. "A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction." Open Engineering 9, no. 1 (2019): 7–13. http://dx.doi.org/10.1515/eng-2019-0006.

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AbstractPractical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

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Correia, Sérgio D., Marko Beko, Luis A. Da Silva Cruz, and Slavisa Tomic. "Elephant Herding Optimization for Energy-Based Localization." Sensors 18, no. 9 (2018): 2849. http://dx.doi.org/10.3390/s18092849.

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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

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Talatahari, Babak, Mahdi Azizi, Siamak Talatahari, Mohamad Tolouei, and Pooya Sareh. "Crystal structure optimization approach to problem solving in mechanical engineering design." Multidiscipline Modeling in Materials and Structures 18, no. 1 (2022): 1–23. http://dx.doi.org/10.1108/mmms-10-2021-0174.

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PurposeIn this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems.Design/methodology/approachA total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems.FindingsThe results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors.Research limitations/implicationsThis paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering.Originality/valueCryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems.

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Cruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Iván Amaya, Yong Shi, Hugo Terashima-Marín, and Nelishia Pillay. "Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems." Mathematics 8, no. 11 (2020): 2046. http://dx.doi.org/10.3390/math8112046.

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Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.

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Barraza, Juan, Luis Rodríguez, Oscar Castillo, Patricia Melin, and Fevrier Valdez. "A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm." Journal of Optimization 2018 (May 27, 2018): 1–18. http://dx.doi.org/10.1155/2018/6495362.

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The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of both methods, respectively, with the goal of obtaining a new hybridization between the Fireworks Algorithm (FWA) and the Grey Wolf Optimizer (GWO), which is denoted as FWA-GWO, and that is presented in more detail in this work. In addition, we are presenting simulation results on a set of problems that were tested in this paper with three different metaheuristics (FWA, GWO, and FWA-GWO) and these problems form a set of 22 benchmark functions in total. Finally, a statistical study with the goal of comparing the three different algorithms through a hypothesis test (Z-test) is presented for supporting the conclusions of this work.

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Yaghini, Masoud, Mohsen Momeni, and Mohammadreza Sarmadi. "A DIMMA-Based Memetic Algorithm for 0-1 Multidimensional Knapsack Problem Using DOE Approach for Parameter Tuning." International Journal of Applied Metaheuristic Computing 3, no. 2 (2012): 43–55. http://dx.doi.org/10.4018/jamc.2012040104.

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Multidimensional 0-1 Knapsack Problem (MKP) is a well-known integer programming problems. The objective of MKP is to find a subset of items with maximum value satisfying the capacity constraints. A Memetic algorithm on the basis of Design and Implementation Methodology for Metaheuristic Algorithms (DIMMA) is proposed to solve MKP. DIMMA is a new methodology to develop a metaheuristic algorithm. The Memetic algorithm is categorized as metaheuristics and is a particular class of evolutionary algorithms. The parameters of the proposed algorithm are tuned by Design of Experiments (DOE) approach. DOE refers to the process of planning the experiment so that appropriate data that can be analyzed by statistical methods will be collected, resulting in valid and objective conclusions. The proposed algorithm is tested on several MKP standard instances from OR-Library. The results show the efficiency and effectiveness of the proposed algorithm.

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Wahab, Hala Bahjat Abdul, Suhad Malallah Kadhem, and Estabraq Abdul Redaa Kadhim. "Proposed Approach for Elliptic Curve Cryptography Based on Metaheuristic Algorithms." International Journal of Scientific Research 2, no. 10 (2012): 1–5. http://dx.doi.org/10.15373/22778179/oct2013/33.

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Cruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Ivan Amaya, and Nelishia Pillay. "Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics." Applied Sciences 11, no. 12 (2021): 5620. http://dx.doi.org/10.3390/app11125620.

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Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

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Augusto, Adriano, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, and Seppe K. L. M. vanden Broucke. "Optimization framework for DFG-based automated process discovery approaches." Software and Systems Modeling 20, no. 4 (2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.

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AbstractThe problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

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Soto, Ricardo, Broderick Crawford, Boris Almonacid, and Fernando Paredes. "Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems." Scientific Programming 2016 (2016): 1–39. http://dx.doi.org/10.1155/2016/9402503.

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The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.

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Klement, Nathalie, Mohamed Amine Abdeljaouad, Leonardo Porto, and Cristóvão Silva. "Lot-Sizing and Scheduling for the Plastic Injection Molding Industry—A Hybrid Optimization Approach." Applied Sciences 11, no. 3 (2021): 1202. http://dx.doi.org/10.3390/app11031202.

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The management of industrial systems is done through different levels, ranging from strategic (designing the system), to tactical (planning the activities and assigning the resources) and operational (scheduling the activities). In this paper, we focus on the latter level by considering a real-world scheduling problem from a plastic injection company, where the production process combines parallel machines and a set of resources. We present a scheduling algorithm that combines a metaheuristic and a list algorithm. Two metaheuristics are tested and compared when used in the proposed scheduling approach: the stochastic descent and the simulated annealing. The method’s performances are analyzed through an experimental study and the obtained results show that its outcomes outperform those of the scheduling policy conducted in a case-study company. Moreover, besides being able to solve large real-world problems in a reasonable amount of time, the proposed approach has a structure that makes it flexible and easily adaptable to several different planning and scheduling problems. Indeed, since it is composed by a reusable generic part, the metaheuristic, it is only required to develop a list algorithm adapted to the objective function and constraints of the new problem to be solved.

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Badr, Elsayed, Mustafa Abdul Salam, Sultan Almotairi, and Hagar Ahmed. "From Linear Programming Approach to Metaheuristic Approach: Scaling Techniques." Complexity 2021 (February 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9384318.

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The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer (WDBC) dataset using the support vector machine (SVM). These scaling techniques are efficient for the linear programming approach. SVM with proposed scaling techniques was applied on the WDBC dataset. The scaling techniques are, namely, arithmetic mean, de Buchet for three cases p = 1,2 , and ∞ , equilibration, geometric mean, IBM MPSX, and Lp-norm for three cases p = 1,2 , and ∞ . The experimental results show that the equilibration scaling technique overcomes the benchmark normalization scaling technique used in many commercial solvers. Finally, the experimental results also show the effectiveness of the grid search technique which gets the optimal parameters (C and gamma) for the SVM classifier.

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Ben Cheikh, Sondes, Christian Tahon, and Slim Hammadi. "An evolutionary approach to solve the dynamic multihop ridematching problem." SIMULATION 93, no. 1 (2016): 3–19. http://dx.doi.org/10.1177/0037549716680025.

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The multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators ( MACGeO), which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers (vehicles) and riders (passengers). Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required.

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Virk, Amandeep K., and Kawaljeet Singh. "On Performance of Binary Flower Pollination Algorithm for Rectangular Packing Problem." Recent Advances in Computer Science and Communications 13, no. 1 (2020): 22–34. http://dx.doi.org/10.2174/2213275911666181114143239.

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Background: Metaheuristic algorithms are optimization algorithms capable of finding near-optimal solutions for real world problems. Rectangle Packing Problem is a widely used industrial problem in which a number of small rectangles are placed into a large rectangular sheet to maximize the total area usage of the rectangular sheet. Metaheuristics have been widely used to solve the Rectangle Packing Problem. Objective: A recent metaheuristic approach, Binary Flower Pollination Algorithm, has been used to solve for rectangle packing optimization problem and its performance has been assessed. Methods: A heuristic placement strategy has been used for rectangle placement. Then, the Binary Flower Pollination Algorithm searches the optimal placement order and optimal layout. Results: Benchmark datasets have been used for experimentation to test the efficacy of Binary Flower Pollination Algorithm on the basis of utilization factor and number of bins used. The simulation results obtained show that the Binary Flower Pollination Algorithm outperforms in comparison to the other well-known algorithms. Conclusion: BFPA gave superior results and outperformed the existing state-of-the-art algorithms in many instances. Thus, the potential of a new nature based metaheuristic technique has been discovered.

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Avila-George, Himer, Jose Torres-Jimenez, Loreto Gonzalez-Hernandez, and Vicente Hernández. "Metaheuristic approach for constructing functional test‐suites." IET Software 7, no. 2 (2013): 104–17. http://dx.doi.org/10.1049/iet-sen.2012.0074.

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Ghasemian, Hadi, Fahimeh Ghasemian, and Hamed Vahdat-Nejad. "Human urbanization algorithm: A novel metaheuristic approach." Mathematics and Computers in Simulation 178 (December 2020): 1–15. http://dx.doi.org/10.1016/j.matcom.2020.05.023.

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Barbosa, Eduardo Batista de Moraes, and Edson Luiz França Senne. "Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms." Journal of Optimization 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/8042436.

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Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters looking for candidate configurations close to a promising alternative. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach.

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Sujaree, Kanon, and Pornthep Sompornpisut. "A Metaheuristic Approach to Transmembrane Protein Assembly Using Limited Distance Restraints." Advanced Materials Research 701 (May 2013): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amr.701.403.

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Metaheuristic methods have become a popular tool in solving large scale optimization problem for a variety of biological systems. In this report, we present Max-Min Ant System (MMAS), a class of swarm intelligence metaheuristics approach, in computing transmembrane helical arrangement of the homotetrameric protein, the potassium channel from Streptomyces iividans (KcsA). The MMAS algorithm was employed to solve transmembrane arrangement problems through the use of an objective penalty function based on distance-violated constraints. Assembly structures of the four inner helices of the KcsA channel were computed bythe construction of probability associated with a set of translational and rotational parameters and the four-fold symmetry transformation applied to the atomic coordinates of the rigid single helix. The MMAS parameters including the number of ants, the number of iteration, weight of pheromone, weight of heuristic information, and pheromone evaporation weight were examined. We demonstrated the effectiveness of the present approach, which can correctly generate native-like structure with root-mean square deviation (RMSD) below 3 Å with respect to the x-ray structure.

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Pereira, Ivo, Ana Madureira, Eliana Costa e Silva, and Ajith Abraham. "A Hybrid Metaheuristics Parameter Tuning Approach for Scheduling through Racing and Case-Based Reasoning." Applied Sciences 11, no. 8 (2021): 3325. http://dx.doi.org/10.3390/app11083325.

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In real manufacturing environments, scheduling can be defined as the problem of effectively and efficiently assigning tasks to specific resources. Metaheuristics are often used to obtain near-optimal solutions in an efficient way. The parameter tuning of metaheuristics allows flexibility and leads to robust results, but requires careful specifications. The a priori definition of parameter values is complex, depending on the problem instances and resources. This paper implements a novel approach to the automatic specification of metaheuristic parameters, for solving the scheduling problem. This novel approach incorporates two learning techniques, namely, racing and case-based reasoning (CBR), to provide the system with the ability to learn from previous cases. In order to evaluate the contributions of the proposed approach, a computational study was performed, focusing on comparing our results previous published results. All results were validated by analyzing the statistical significance, allowing us to conclude the statistically significant advantage of the use of the novel proposed approach.

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Palominos, Pedro, Carla Ortega, Miguel Alfaro, et al. "Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem." Complexity 2022 (October 11, 2022): 1–17. http://dx.doi.org/10.1155/2022/8903005.

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Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem.

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Yun, YoungSu, Mitsuo Gen, and Tserengotov Nomin Erdene. "Applying GA-PSO-TLBO approach to engineering optimization problems." Mathematical Biosciences and Engineering 20, no. 1 (2022): 552–71. http://dx.doi.org/10.3934/mbe.2023025.

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<abstract> <p>Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems. In this paper, a hybrid metaheuristic approach is proposed for solving engineering optimization problems. A genetic algorithm (GA), particle swarm optimization (PSO), and teaching and learning-based optimization (TLBO), called the GA-PSO-TLBO approach, is used and demonstrated for the proposed hybrid metaheuristic approach. Since each approach has its strengths and weaknesses, the GA-PSO-TLBO approach provides an optimal strategy that maintains the strengths as well as mitigates the weaknesses, as needed. The performance of the GA-PSO-TLBO approach is compared with those of conventional approaches such as single metaheuristic approaches (GA, PSO and TLBO) and hybrid metaheuristic approaches (GA-PSO and GA-TLBO) using various types of engineering optimization problems. An additional analysis for reinforcing the performance of the GA-PSO-TLBO approach was also carried out. Experimental results proved that the GA-PSO-TLBO approach outperforms conventional competing approaches and demonstrates high flexibility and efficiency.</p> </abstract>

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Khamoudj, Charaf Eddine, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Mohamed Benbouzid, and Abdenaser Djaafri. "A Learning Variable Neighborhood Search Approach for Induction Machines Bearing Failures Detection and Diagnosis." Energies 13, no. 11 (2020): 2953. http://dx.doi.org/10.3390/en13112953.

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This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process. The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach.

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Almonacid, Boris. "AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning." Entropy 24, no. 7 (2022): 957. http://dx.doi.org/10.3390/e24070957.

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Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find a solution in a reasonable amount of time. However, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly presents a problem. The proposal described in this article contemplates an approach that automatically creates metaheuristic algorithms given a set of optimisation problems. These metaheuristic algorithms are created by modifying their logical structure via the execution of an evolutionary process. This process employs an extension of the reinforcement learning approach that considers multi-agents in their environment, and a learning agent composed of an analysis process and a process of modification of the algorithms. The approach succeeded in creating a metaheuristic algorithm that managed to solve different continuous domain optimisation problems from the experiments performed. The implications of this work are immediate because they describe a basis for the generation of metaheuristic algorithms in an online-evolution.

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Gunay-Sezer, Noyan Sebla, Emre Cakmak, and Serol Bulkan. "A Hybrid Metaheuristic Solution Method to Traveling Salesman Problem with Drone." Systems 11, no. 5 (2023): 259. http://dx.doi.org/10.3390/systems11050259.

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The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics.

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Fernandes, João M. R. C., Seyed Mahdi Homayouni, and Dalila B. M. M. Fontes. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review." Sustainability 14, no. 10 (2022): 6264. http://dx.doi.org/10.3390/su14106264.

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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Ivanovic, Marija, Aleksandar Savic, Dragan Urosevic, and Djordje Dugosija. "A new variable neighborhood search approach for solving dynamic memory allocation problem." Yugoslav Journal of Operations Research 28, no. 3 (2018): 291–314. http://dx.doi.org/10.2298/yjor161015018i.

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This paper is devoted to the Dynamic Memory Allocation Problem (DMAP) in embedded systems. The existing Integer Linear Programing (ILP) formulation for DMAP is improved, and given that there are several metaheuristic approaches for solving the DMAP, a new metaheuristic approach is proposed and compared with the former ones. Computational results show that our new heuristic approach outperforms the best algorithm found in the literature regarding quality and running times.

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Postolov, Borche, Nikolay Hinov, Atanas Iliev, and Dimitar Dimitrov. "Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm." Energies 15, no. 16 (2022): 5989. http://dx.doi.org/10.3390/en15165989.

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This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem.

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Kim, Jinho, Chang Seob Kim, and Zong Woo Geem. "A Memetic Approach for Improving Minimum Cost of Economic Load Dispatch Problems." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/906028.

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Economic load dispatch problem is a popular optimization problem in electrical power system field, which has been so far tackled by various mathematical and metaheuristic approaches including Lagrangian relaxation, branch and bound method, genetic algorithm, tabu search, particle swarm optimization, harmony search, and Taguchi method. On top of these techniques, this study proposes a novel memetic algorithm scheme combining metaheuristic algorithm and gradient-based technique to find better solutions for an economic load dispatch problem with valve-point loading. Because metaheuristic algorithms have the strength in global search and gradient-based techniques have the strength in local search, the combination approach obtains better results than those of any single approach. A bench-mark example of 40 generating-unit economic load dispatch problem demonstrates that the memetic approach can further improve the existing best solutions from the literature.

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Chandra, Agung, and Aulia Naro. "S-Metaheuristics Approach to Solve Traveling Salesman Problem." Jurnal METRIS 21, no. 02 (2020): 111–15. http://dx.doi.org/10.25170/metris.v21i02.2496.

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Metaheuristic algorithm is a state of the art optimization method which suitable for solving large and complex problem. Single solution technique – Smetaheuristic is one of metaheuristic algorithm that search near optimal solution and known as exploitation based. The research conducted to seek a better solution for deliverying goods to 29 destinations by comparing two well known optimization methods that can produce the shortest distance: Simulated Annealing (SA) and Tabu Search (TS). The result shows that TS – 107 KM has a shorter distance than SA – 119 KM. Exploration based method should be conducted for next research to produce information in which one is a better method

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Bhanu, S. Mary Saira, and N. P. Gopalan. "A Hyper-Heuristic Approach for Efficient Resource Scheduling in Grid." International Journal of Computers Communications & Control 3, no. 3 (2008): 249. http://dx.doi.org/10.15837/ijccc.2008.3.2393.

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Efficient execution of computations in grid can require mapping of tasks to processors whose performance is both irregular and time varying because of dynamic nature. The task of mapping jobs to the available computing nodes or scheduling of the jobs on the grid is a NP complete problem. The NP-hard problem is often solved using heuristics techniques. Heuristic and metaheuristic approaches tend to be knowledge rich, requiring substantial expertise in both the problem domain and appropriate heuristics techniques. To alleviate this problem the concept of Hyperheuristic was introduced. They operate on the search space of heuristics instead of candidate solutions and can be applied to any optimization problem. This paper emphasizes the use of Hyper-heuristics built on top of hybridized Metaheuristics to efficiently and effectively schedule jobs onto available resources in a grid environment thus resulting in an optimal schedule with minimum makespan.

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Penas, David R., and Marcos Raydan. "A metaheuristic penalty approach for the starting point in nonlinear programming." RAIRO - Operations Research 54, no. 2 (2020): 451–69. http://dx.doi.org/10.1051/ro/2019096.

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Solving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility.

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Alfarhisi, Zikrie Pramudia, Hadi Suyono, and Fakhriy Hario Partiansyah. "4G LTE Network Coverage Optimization Using Metaheuristic Approach." International Journal of Computer Applications Technology and Researc 10, no. 01 (2021): 010–13. http://dx.doi.org/10.7753/ijcatr1001.1003.

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The main focus of this paper is to optimize the coverage of each 4G LTE network cell within the service area. There are many algorithms can be implemented to determine the optimal 4G LTE coverage area including the deterministic and heuristic approaches. The deterministic approach could solve accurately the optimization problem but need more resources and time consuming to determine the convergence parameters. Therefore, the heuristic approaches were introduced to improve the deterministic approach drawback. The methods used are the Differential Evolution Algorithm (DEA) and Adaptive Mutation Genetic Algorithm (AMGA), which are categorized as metaheuristic approach. The DEA and AMGA algorithms have been widely used to solve combinatorial problems, including for solving the network optimizations. In the network optimization, coverage is strongly related to 2 objectives, which are reducing the black spot area and decreasing the overlapping coverage areas. Coverage overlap is a condition when some cell sites in an area overlap. It implies in the occurrence of hand off and an inefficient network management. This research aims to obtain an optimal 4G LTE network coverage and reduce the overlapping coverage areas based on effective e-Node B arrangements by using the DEA and AMGA algorithms. The simulations results showed that the DEA algorithm’s coverage effectiveness was 23,4%, and the AMGA Algorithm’s was 16,32%.

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Mahmoudzadeh, Houra, and Kourosh Eshghi. "A Metaheuristic Approach to the Graceful Labeling Problem." International Journal of Applied Metaheuristic Computing 1, no. 4 (2010): 42–56. http://dx.doi.org/10.4018/jamc.2010100103.

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In graph theory, a graceful labeling of a graph G = (V, E) with n vertices and m edges is a labeling of its vertices with distinct integers between 0 and m inclusive, such that each edge is uniquely identified by the absolute difference between its endpoints. In this paper, the well-known graceful labeling problem of graphs is represented as an optimization problem, and an algorithm based on Ant Colony Optimization metaheuristic is proposed for finding its solutions. In this regard, the proposed algorithm is applied to different classes of graphs and the results are compared with the few existing methods inside of different literature.

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Korošec, Peter, and Gregor Papa. "Metaheuristic approach to transportation scheduling in emergency situations." Transport 28, no. 1 (2013): 46–59. http://dx.doi.org/10.3846/16484142.2013.781540.

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Mesquita, Marta, Alberto G. Murta, Ana Paias, and Laura Wise. "A metaheuristic approach to fisheries survey route planning." International Transactions in Operational Research 24, no. 3 (2016): 439–64. http://dx.doi.org/10.1111/itor.12252.

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Malaguti, Enrico, Michele Monaci, and Paolo Toth. "A Metaheuristic Approach for the Vertex Coloring Problem." INFORMS Journal on Computing 20, no. 2 (2008): 302–16. http://dx.doi.org/10.1287/ijoc.1070.0245.

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Bhanja, Urmila, and Sudipta Mahapatra. "A metaheuristic approach for optical network optimization problems." Applied Soft Computing 13, no. 2 (2013): 981–97. http://dx.doi.org/10.1016/j.asoc.2012.09.011.

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Mohapatra, Soumya Snigdha, Rakesh Ranjan Kumar, Mamdouh Alenezi, Abu Taha Zamani, and Nikhat Parveen. "QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach." Electronics 11, no. 21 (2022): 3469. http://dx.doi.org/10.3390/electronics11213469.

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As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster.

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Dražić, Zorica, Mirjana Čangalović, and Vera Kovačević-Vujčić. "A metaheuristic approach to the dominating tree problem." Optimization Letters 11, no. 6 (2016): 1155–67. http://dx.doi.org/10.1007/s11590-016-1017-5.

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Sawant, Shrutika, and Prabukumar Manoharan. "Hyperspectral band selection based on metaheuristic optimization approach." Infrared Physics & Technology 107 (June 2020): 103295. http://dx.doi.org/10.1016/j.infrared.2020.103295.

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Madhusudhanan, B., P. Sumathi, N. Shunmuga Karpagam, A. Mahesh, and P. Anlet Pamila Suhi. "An hybrid metaheuristic approach for efficient feature selection." Cluster Computing 22, S6 (2018): 14541–49. http://dx.doi.org/10.1007/s10586-018-2337-2.

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Perez-Rodriguez, Ricardo. "An estimation of distribution algorithm for combinatorial optimization problems." International Journal of Industrial Optimization 3, no. 1 (2022): 47–67. http://dx.doi.org/10.12928/ijio.v3i1.5862.

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This paper considers solving more than one combinatorial problem considered some of the most difficult to solve in the combinatorial optimization field, such as the job shop scheduling problem (JSSP), the vehicle routing problem with time windows (VRPTW), and the quay crane scheduling problem (QCSP). A hybrid metaheuristic algorithm that integrates the Mallows model and the Moth-flame algorithm solves these problems. Through an exponential function, the Mallows model emulates the solution space distribution for the problems; meanwhile, the Moth-flame algorithm is in charge of determining how to produce the offspring by a geometric function that helps identify the new solutions. The proposed metaheuristic, called HEDAMMF (Hybrid Estimation of Distribution Algorithm with Mallows model and Moth-Flame algorithm), improves the performance of recent algorithms. Although knowing the algebra of permutations is required to understand the proposed metaheuristic, utilizing the HEDAMMF is justified because certain problems are fixed differently under different circumstances. These problems do not share the same objective function (fitness) and/or the same constraints. Therefore, it is not possible to use a single model problem. The aforementioned approach is able to outperform recent algorithms under different metrics for these three combinatorial problems. Finally, it is possible to conclude that the hybrid metaheuristics have a better performance, or equal in effectiveness than recent algorithms.

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Kavoosi, Masoud, Maxim A. Dulebenets, Olumide Abioye, et al. "Berth scheduling at marine container terminals." Maritime Business Review 5, no. 1 (2019): 30–66. http://dx.doi.org/10.1108/mabr-08-2019-0032.

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Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

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Arık, Oğuzhan Ahmet, and Mehmet Duran Toksarı. "A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration." International Journal of Applied Metaheuristic Computing 12, no. 3 (2021): 195–211. http://dx.doi.org/10.4018/ijamc.2021070109.

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This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem.

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Aviles, Marcos, Juvenal Rodríguez-Reséndiz, and Danjela Ibrahimi. "Optimizing EMG Classification through Metaheuristic Algorithms." Technologies 11, no. 4 (2023): 87. http://dx.doi.org/10.3390/technologies11040087.

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This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.

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Q. Salih, Sinan, and Abdul Rahman A. Alsewari. "Solving large-scale problems using multi-swarm particle swarm approach." International Journal of Engineering & Technology 7, no. 3 (2018): 1725. http://dx.doi.org/10.14419/ijet.v7i3.14742.

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Several metaheuristics have been previously proposed and several improvements have been implemented as well. Most of these methods were either inspired by nature or by the behavior of certain swarms such as birds, ants, bees, or even bats. In the metaheuristics, two key components (exploration and exploitation) are significant and their interaction can significantly affect the efficiency of a metaheuristic. How-ever, there is no rule on how to balance these important components. In this paper, a new balancing mechanism based on multi-swarm approach is proposed for balancing exploration and exploitation in metaheuristics. The new approach is inspired by the concept of a group(s) of people controlled by their leader(s). The leaders of the groups communicate in a meeting room where the overall best leader makes the final decisions. The proposed approach applied on Particle Swarm Optimization (PSO) to balance the exploration and exploitation search called multi-swarm cooperative PSO (MPSO). The proposed approach strived to scale up the application of the (PSO) algorithm towards solving large-scale optimization tasks of up to 1000 real-valued variables. In the simulation part, several benchmark functions were per-formed with different numbers of dimensions. The proposed algorithm was tested on several test functions, with four different number of dimensions (100, 500, and 1000) it was evaluated in terms of performance efficiency and compared to standard PSO (SPSO), and master-salve PSO algorithm. The results showed that the proposed PSO algorithm outperformed the other algorithms in terms of the optimal solutions and the convergence.

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Navarro-Acosta, Jesús Alejandro, Irma D. García-Calvillo, Vanesa Avalos-Gaytán, and Edgar O. Reséndiz-Flores. "Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes." Applied Sciences 10, no. 24 (2020): 9145. http://dx.doi.org/10.3390/app10249145.

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

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