Article
Computer Science, Artificial Intelligence
Changting Zhong, Gang Li, Zeng Meng
Summary: This paper presents a novel swarm-based metaheuristic algorithm called beluga whale optimization (BWO), which is inspired by the behaviors of beluga whales, for solving optimization problems. BWO consists of three phases: exploration, exploitation, and whale fall, corresponding to pair swim, prey, and whale fall behaviors, respectively. The self-adaptive balance factor and probability of whale fall in BWO play significant roles in controlling the exploration and exploitation capabilities. Additionally, Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of BWO is evaluated using 30 benchmark functions and compared with 15 other metaheuristic algorithms through qualitative, quantitative, and scalability analysis. The results show that BWO is a competitive algorithm for solving unimodal and multimodal optimization problems. Furthermore, BWO achieves the first overall rank in the scalability analysis of benchmark functions among the compared metaheuristic algorithms. Four engineering problems are also solved to demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is publicly available.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Amir Seyyedabbasi, Farzad Kiani
Summary: The study introduces a new metaheuristic algorithm, SCSO, which mimics the behavior of sand cats. The algorithm performs well in finding good solutions and outperforms compared methods in various test functions and engineering design problems.
ENGINEERING WITH COMPUTERS
(2023)
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
Automation & Control Systems
Adam Slowik, Krzysztof Cpalka
Summary: This article presents the issues and applications of hybrid nature-inspired population-based algorithms for global optimization. It introduces the concept of nature-inspired population-based optimization methods and their hybridization with other techniques. Concrete examples are used to illustrate each type of hybridization. The popularity and common application areas of selected hybrid algorithms are demonstrated. The article also discusses the computational complexity and industrial applications of these algorithms, as well as the inherent problems and research directions in this field.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Amany H. Abou El-Naga, Sabah Sayed, Akram Salah, Heba Mohsen
Summary: Single-cell RNA sequencing technology reveals the disparity of cellular heterogeneity and helps researchers understand the expression profiles and cellular differentiation of individual cells. This paper proposes a computational clustering approach based on consensus clustering using swarm intelligent optimization algorithms to accurately identify cell subtypes. The approach utilizes variational auto-encoders to handle dimensionality curse and applies swarm intelligent algorithms for clustering in a latent feature space. The proposed method automatically determines the number of clusters without prior knowledge. Experimental results demonstrate better performance compared to existing tools, achieving high adjusted rand index scores for various datasets.
Review
Engineering, Chemical
Malini Deepak, Rabee Rustum
Summary: The activated sludge process (ASP) is widely used in biological wastewater treatment. Advances in research have introduced Artificial Intelligence (AI), specifically Nature-Inspired Algorithm (NIA) techniques, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), to optimize ASP systems. This paper discusses the latest NIAs used in ASP and compares their advantages and limitations to traditional algorithms.
Review
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh, Iyad Abu Doush, Mohammed A. Awadallah, Ammar Kamal Abasi, Ashraf Elnagar
Summary: This paper reviews the research conducted using the bat-inspired algorithm (BA) from 2017 to 2021, summarizing its characteristics, development, and applications. The limitations of BA are also analyzed, and suggestions for future directions and improvements are given.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Zounemat-Kermani, Amin Mahdavi-Meymand, Reinhard Hinkelmann
Summary: In this study, a combination of firefly algorithm (FA) and butterfly optimization algorithm (BOA) was used with adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) models for optimal prediction of sediment volumetric concentration (Cv) in sewer systems. The integration of FA and BOA significantly improved the performance of ANFIS in modeling the process of Cv prediction, while slightly optimizing the performance of GMDH.
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
Automation & Control Systems
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: This paper compares Particle Swarm Optimization and Differential Evolution, two landmark metaheuristics, and finds that the performance of Differential Evolution algorithms is clearly better than Particle Swarm Optimization ones. Despite being more commonly used in the literature, Particle Swarm Optimization algorithms are outperformed by Differential Evolution on single-objective numerical benchmarks and real-world problems. Therefore, there is a need to reconsider the algorithmic philosophy of Particle Swarm Optimization variants to enhance their competitiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Chrysanthos Maraveas, Panagiotis G. Asteris, Konstantinos G. Arvanitis, Thomas Bartzanas, Dimitrios Loukatos
Summary: The article provides an overview of four major bioinspired intelligent algorithms used in agriculture, including ecological algorithms, swarm-intelligence-based algorithms, ecology-based algorithms, and multi-objective algorithms. The focus is on the variants of swarm intelligence algorithms, such as artificial bee colony, genetic algorithm, flower pollination algorithm, particle swarm, ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm, as they are widely used in the agricultural sector. Scholars generally agree that certain variants of these bioinspired algorithms are more effective than others in specific applications, such as farm machinery path optimization and pest detection. While the adoption of hyper-heuristic algorithms in agriculture remains low, the benefits associated with these algorithms, such as fuel and cost savings, improved accuracy in agricultural processes, outweigh the risks.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
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
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: This study tested the performance of 73 optimization algorithms on four problem sets with different dimensionalities. The results showed that algorithms performing best on older benchmark sets were different from those performing best on CEC 2020 benchmark set. The choice of benchmark set can have a significant impact on the ranking of algorithms, and tuning control parameters can improve algorithm performance.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Mathew Mithra Noel, Venkataraman Muthiah-Nakarajan, Geraldine Bessie Amali, Advait Sanjay Trivedi
Summary: The Firebug Swarm Optimization (FSO) algorithm, inspired by the reproductive swarming behavior of Firebugs, outperforms 17 popular state-of-the-art heuristic global optimization algorithms on benchmark tests, demonstrating its effectiveness in finding optimal solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Mahdi Valikhan Anaraki, Saeed Farzin
Summary: This study presents a new natural-based algorithm called the Humboldt Squid Optimization Algorithm (HSOA), inspired by the hunting, moving, and mating behavior of Humboldt squids. HSOA addresses existing issues through processes such as attacking, escaping, successful attacks, larger squids attacking smaller ones, and mating. By connecting and cooperating, individuals in HSOA achieve optimal responses, making it versatile and applicable to mathematical and engineering problems. The study demonstrates that HSOA outperforms other algorithms in benchmark function problems and engineering problems.
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)