Review
Mathematics
Faten Aljalaud, Heba Kurdi, Kamal Youcef-Toumi
Summary: This study provides a comprehensive review of path planning algorithms for multi-UAV systems, with a focus on bio-inspired algorithms. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The classification framework is significant for researchers to compare algorithm designs, understand current research trends, and anticipate future directions.
Article
Computer Science, Artificial Intelligence
Ryan Solgi, Hugo A. Loaiciga
Summary: This study evaluates the performance of seven bee-inspired metaheuristic algorithms in solving continuous optimization problems, ranks them based on convergence efficiency, and identifies ABC, BEGA, and MBO as the most efficient algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Weiguo Zhao, Liying Wang, Zhenxing Zhang, Honggang Fan, Jiajie Zhang, Seyedali Mirjalili, Nima Khodadadi, Qingjiao Cao
Summary: The electric eel foraging optimization (EEFO) algorithm is a swarm-based, bio-inspired metaheuristic algorithm that imitates the foraging behaviors of electric eels. Through mathematical modeling, EEFO provides both exploration and exploitation abilities during the optimization process. Experimental results show that EEFO outperforms other algorithms in various tests, especially in optimization problems with unimodal characteristics and many constraints and variables.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Andrey Grabovsky, Vitaly Vanchurin
Summary: In this study, we analyze the algorithmic and computational aspects of biological phenomena in the context of machine learning. We develop two machine learning algorithms, the replication algorithm and the programmed death algorithm, using different measures of neuron efficiency. We demonstrate the computational advantages of these bio-inspired algorithms by training feedforward neural networks on the MNIST dataset of handwritten images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Ouail Mjahed, Salah El Hadaj, El Mahdi El Guarmah, Soukaina Mjahed
Summary: This paper investigates the optimization effects of multiple bio-inspired metaheuristic algorithms on hybrid ANNs, proposing a method that takes into account different datasets. Through classification experiments on four datasets, the results show that networks based on PSO-ANN have higher efficiency values.
STUDIES IN INFORMATICS AND CONTROL
(2022)
Article
Automation & Control Systems
Ouail Mjahed, Salah El Hadaj, El Mahdi El Guarmah, Soukaina Mjahed
Summary: This paper investigates the use of multiple hybrid artificial neural networks (ANNs) to classify different types of datasets, and optimizes the efficiency of the neural networks using multiple bio-inspired metaheuristic algorithms. The results show that the metaheuristic algorithms can achieve optimal efficiency, with PSO-ANN-based networks performing the best.
STUDIES IN INFORMATICS AND CONTROL
(2022)
Article
Computer Science, Interdisciplinary Applications
Gaurav Dhiman
Summary: In this paper, a hybrid bio-inspired metaheuristic optimization approach named Emperor Penguin and Salp Swarm Algorithm (ESA) is proposed. The efficiency of the ESA is evaluated through various analyses on 53 benchmark test functions, showing that it offers optimal solutions compared to other competitor algorithms. The robustness of ESA is also demonstrated through its application on six constrained and one unconstrained engineering problems.
ENGINEERING WITH COMPUTERS
(2021)
Article
Mathematics
Alvaro Gomez-Rubio, Ricardo Soto, Broderick Crawford, Adrian Jaramillo, David Mancilla, Carlos Castro, Rodrigo Olivares
Summary: Solving complex big data and constraint problems in the field of optimization is difficult. This paper proposes a multiprocessing approach that combines clustering and parallelism to improve the search process of metaheuristics. Machine learning algorithms are used to enhance the segmentation of the search space. Experimental results show that this approach is competitive in solving large-scale optimization problems.
Article
Computer Science, Artificial Intelligence
Malik Shehadeh Braik
Summary: The paper introduces a novel meta-heuristic algorithm called Chameleon Swarm Algorithm (CSA) for global numerical optimization problems, inspired by the foraging behavior of chameleons. The CSA was evaluated on benchmark test functions and outperformed other meta-heuristic algorithms in terms of optimization accuracy, demonstrating its applicability in solving real-world engineering design problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Yuxin Jiang, Qing Wu, Shenke Zhu, Luke Zhang
Summary: This paper proposes a novel bio-inspired algorithm called Orca Predation Algorithm (OPA) that simulates the hunting behavior of orcas and abstracts it into several mathematical models to balance the exploitation and exploration stages. The algorithm demonstrates superior performance in generating promising results compared to other test algorithms across different search landscapes.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Youcef Azzoug, Abdelmadjid Boukra
Summary: This paper presents a recapitulation of the historical evolution and a future overview of all vehicular ad-hoc network (VANET) routing problems, exploring bio-inspired optimization methods for solving various VANET routing issues. It provides insight into the nature of different routing problems, the range of studies, and the types of metaheuristics used for optimization, guiding the future research direction in this field.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Information Systems
R. Yesodha, T. Amudha
Summary: This research uses an improved bio-inspired metaheuristic called firefly to solve the Multi-Depot Vehicle Routing Problem with Time Windows (MDVRP-TW). The objective is to minimize the overall distance and means of transportation while satisfying various constraints. The research combines different algorithms and heuristic methods to optimize the routes and proves the effectiveness of the improved firefly algorithm.
COMPUTER COMMUNICATIONS
(2022)
Review
Computer Science, Information Systems
Tin H. H. Pham, Bijan Raahemi
Summary: Based on the principles of biological evolution, bio-inspired algorithms are becoming popular for optimizing robust techniques. These algorithms, unlike gradient descent methods, are computationally efficient and perform well with nonlinear and high-dimensional data. The objective of this study is to understand the algorithms, application domains, effectiveness, and challenges of bio-inspired feature selection techniques. A systematic literature review was conducted and 38 articles were selected. The findings suggest that future research should focus on applying bio-inspired feature selection to diverse applications and explore enhancement techniques and alternative transfer functions.
Article
Biology
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee, Noha F. Mahmoud, Marwa M. Emam
Summary: This paper introduces a new bio-inspired optimization algorithm called the Liver Cancer Algorithm (LCA), which provides efficient search and exploration methods by simulating the growth and spread of liver tumors. Experimental results show that the LCA algorithm outperforms other methods in handling mathematical benchmark problems and feature selection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Multidisciplinary
Souad Larabi-Marie-Sainte
Summary: This study proposed four new feature selection methods based on outlier detection using the Projection Pursuit method. These methods improved classification accuracy rate by an average of 6.64% and outperformed state-of-the-art methods on most datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Fabio Lobato, Claudomiro Sales, Igor Araujo, Vincent Tadaiesky, Lilian Dias, Leonardo Ramos, Adamo Santana
PATTERN RECOGNITION LETTERS
(2015)
Article
Engineering, Electrical & Electronic
Igor Araujo, Vincent Tadaiesky, Diego Cardoso, Yoshikazu Fukuyama, Adamo Santana
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2019)
Article
Energy & Fuels
Gabriel Vianna Soares Rocha, Raphael Pablo de Souza Barradas, Joao Rodrigo Silva Muniz, Ubiratan Holanda Bezerra, Igor Meireles de Araujo, Daniel de Souza Avelar da Costa, Abner Cardoso da Silva, Marcus Vinicius Alves Nunes, Jucileno Silva e Silva
Article
Computer Science, Information Systems
Igor Araujo, Carlos Natalino, Diego Cardoso
Summary: NFV advocates replacing specific-purpose hardware with general-purpose hardware to reduce costs and increase network operation flexibility. GPUs deployed in data centers can accelerate the packet processing capability of vNFs, but may introduce delays. Our framework proposes using GPUs to support vNF execution for improved throughput and reduced CPU resource usage.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Joao Rodrigo Da Silva Muniz, Gabriel Vianna Soares Rocha, Raphael Pablo De Souza Barradas, Igor Meireles De Araujo, Daniel De Souza Avelar Da Costa, Ubiratan Holanda Bezerra, Marcus Vinicius Alves Nunes, Jucileno Silva e Silva
Proceedings Paper
Engineering, Electrical & Electronic
Igor M. Araujo, Carlos Natalino, Adamo L. Santana, Diego L. Cardoso
2018 20TH ANNIVERSARY INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON)
(2018)