Results 21 to 30 of about 11,900 (220)
Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm
IEEE Access, 2019 The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has become an important tool for solving most of the toughest optimization problems in almost all areas of global optimization and engineering practices.
Moyinoluwa B. Agbaje +2 more
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Parallel Hybrid Island Metaheuristic Algorithm
IEEE Access, 2022 This study introduces a novel Parallel Hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base.
Jiawei Li, Tad Gonsalves
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Implementing Metaheuristic Optimization Algorithms with JECoLi [PDF]
2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009 This work proposes JECoLi - a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and ...
Pedro Evangelista +2 more
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Buildings, 2022 This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm’s accuracy and local convergence in finite element (FE) model updating.
Shiqiang Qin +3 more
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Metaheuristics in nature-inspired algorithms [PDF]
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 To many people, the terms nature-inspired algorithm and metaheuristic are interchangeable. However, this contemporary usage is not consistent with the original meaning of the term metaheuristic, which referred to something closer to a design pattern than to an algorithm.
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A Hybrid Algorithm for Metaheuristic Optimization
CoRR, 2019 We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess.
Sujit Pramod Khanna +1 more
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An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems [PDF]
Journal of Advances in Computer Engineering and Technology, 2020 Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near ...
narges jafari +1 more
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Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters
International Journal of Photoenergy, 2021 The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms.
Daniel T. Cotfas +3 more
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Complexity, 2020 For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm.
Pisut Pongchairerks
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Overview of Metaheuristic Algorithms
Polaris Global Journal of Scholarly Research and Trends, 2023 Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem.
Saman M. Almufti +5 more
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