Results 31 to 40 of about 24,415 (308)
Open Mathematics, 2017 Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a ...
Bansal Shonak +2 more
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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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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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Applied Sciences, 2022 Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization ...
Yaw-Hong Kang, Jau-Wen Lin, Wei-Chen You
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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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Why Tuning the Control Parameters of Metaheuristic Algorithms Is So Important for Fair Comparison?
Mendel, 2020 Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals.
Anezka Kazikova +2 more
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Next Generation Metaheuristic: Jaguar Algorithm
IEEE Access, 2018 Metaheuristic algorithms are implemented to solve optimization problems and have recently received significant research attention. Metaheuristic algorithms rely primarily on two properties, exploration, and exploitation.
Yao-Hsin Chou +3 more
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, 2023 This analytical report delves into the comprehensive evaluation of both metaheuristic and non-metaheuristic algorithms utilized for clustering in wireless networks.
Nishant Tripathi, et al.
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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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Trigonometric words ranking model for spam message classification
IET Networks, EarlyView., 2022 Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when
Suha Mohammed Hadi +7 more
wiley +1 more source