Article
Computer Science, Artificial Intelligence
Sarah E. Shukri, Rizik Al-Sayyed, Amjad Hudaib, Seyedali Mirjalili
Summary: Cloud computing is a popular technology that enables users to remotely access computing resources in a pay-as-you-go model. Task scheduling is a primary challenge in cloud computing environments, with many meta-heuristic algorithms like MVO and PSO being used. The Enhanced Multi-Verse Optimizer (EMVO) proposed in this paper outperforms both MVO and PSO algorithms in terms of minimizing makespan time and increasing resource utilization in cloud environments.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Srinivasa Acharya, S. Ganesan, D. Vijaya Kumar, S. Subramanian
Summary: This paper proposes a multi-objective multi-verse optimization scheme to minimize the dynamic economic load dispatch issue, including valve-point effects. The algorithm maintains the ramp of unit required rate constraint to eliminate discontinuity in power system operation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Telecommunications
Sivakumar Pothiraj, Jeya Prakash Kadambarajan, Pandiaraj Kadarkarai
Summary: The study utilizes a hybrid multi verse optimizer floor planning method to adjust node coordinates and dimensions, achieving optimized floor planning with minimal wire length and area.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Remote Sensing
Shrutika S. Sawant, Manoharan Prabukumar, Agilandeeswari Loganathan, Farhan A. Alenizi, Subodh Ingaleshwar
Summary: This article introduces a novel unsupervised band selection approach called MOMVOBS, which uses a multi-objective multi-verse optimizer to optimize the information richness, redundancy, and number of selected bands simultaneously. The experimental results demonstrate that the proposed approach outperforms other methods in selecting highly informative bands and achieves higher classification accuracy with a small number of retained bands.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Energy & Fuels
Xin Li, Fengrong Bi, Lipeng Zhang, Xiao Yang, Guichang Zhang
Summary: This paper presents an efficient pattern recognition method for engine fault detection using echo state network (ESN) and multi-verse optimizer (MVO). The method employs bispectrum to transform the vibration signal into a two-dimensional matrix, and designs a sparse input weight-generating algorithm for ESN. The deep ESN model is built by fusing fixed convolution kernels and an autoencoder, and the MVO is improved with a novel traveling distance rate and collapse mechanism to optimize local search. The experimental results show that the proposed method achieves a recognition rate of 93.10% in complex engine faults, outperforming traditional deep belief networks, convolutional neural networks, LSTM network, and GRU. This method has great potential for the end-to-end detection of rotating machinery faults.
Article
Computer Science, Artificial Intelligence
Nima Khodadadi, Laith Abualigah, Seyedali Mirjalili
Summary: The single-objective version of the Stochastic Paint Optimizer (SPO) has been modified to address multi-objective optimization problems and is now known as MOSPO. SPO utilizes color theory, the color wheel, and color combination methods to achieve excellent exploration and exploitation capabilities. By using four simple color combination rules without internal parameters and incorporating principles like a fixed-sized external archive, the recommended MOSPO technique differs from the original single-objective SPO. Furthermore, a leader selection feature has been added to SPO to accommodate multi-objective optimization. Testing performed on various mathematical and engineering design problems demonstrates that MOSPO outperforms other multi-objective optimization algorithms such as MOPSO, MSSA, and multi-objective ant lion optimizer in terms of precision and uniformity. Based on different performance metrics including generational distance, inverted generational distance, maximum spread, and spacing, the proposed algorithm consistently produces high-quality Pareto fronts with competitive convergence.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
R. Rajeswari, G. Neelima, Balajee Maram, Anupama Angadi
Summary: Brain tumor is a severe nervous disorder that can cause serious damage and even death. Classifying brain tumors early is crucial for increasing the survival rate of patients. A deep learning classifier called Deep Maxout network is developed to accurately classify brain tumors into different grades. Using this classification result, features related to tumor grades are obtained to predict patient survival. Deep learning is an effective and robust model for tumor classification and detection using MRI. The Deep Long Short-Term Memory (LSTM) classifier is used for survival prediction with high performance achieved (accuracy: 0.9434, sensitivity: 0.9324, specificity: 0.9202, prediction error: 0.0579).
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Mohammad Hashem Ryalat, Hussam N. Fakhouri, Jamal Zraqou, Faten Hamad, Mamon S. Alzboun, Ahmad K. Al Hwaitat
Summary: Data testing is crucial in software development, and this study proposes an improved version of the Multi-verse Optimizer called TMVO. TMVO considers the movement of the swarm and the mean of the two best solutions in the universe, ensuring efficient exploration and exploitation. It is applied to automatically develop test cases for structural data testing, with a focus on automating the data collection process. Tested on various functions and challenging programs, TMVO outperformed the original MVO algorithm in most cases.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
Yan Han, Weibin Chen, Ali Asghar Heidari, Huiling Chen, Xin Zhang
Summary: This paper proposes the CBQMVO algorithm, which extends the original MVO algorithm by introducing three strategies to address the issues of slow convergence speed and falling into local optimum. Experimental results demonstrate that CBQMVO performs well on some unimodal and complex competition functions, and achieves better segmentation effect in breast cancer pathologic image segmentation compared to other metaheuristic algorithms.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Natee Panagant, Ghanshyam G. Tejani, Nantiwat Pholdee, Sujin Bureerat, Nikunj Mashru, Pinank Patel
Summary: Multi-objective structure optimization is a complex design issue that involves dealing with multiple conflicting objectives and various constraints. A powerful optimizer called MOMVO2arc has been proposed and evaluated for solving large structure optimization problems with less computation time.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mohammed Otair, Areej Alhmoud, Heming Jia, Maryam Altalhi, Ahmad MohdAziz Hussein, Laith Abualigah
Summary: This paper introduces an improved multiobjective multi-verse optimizer (IMOMVO) to solve task scheduling problems by dynamically enhancing the equation of updating the average positioning. Results show that IMOMVO outperforms the original MVO and its latest enhanced version mMVO in terms of shorter execution time, higher throughput, and stronger Vm processing power.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Thermodynamics
Navid Nazari, Seyedmostafa Mousavi, Seyedali Mirjalili
Summary: This study proposes an innovative configuration based on solar pre-heating and biomass direct combustion for combined production of electricity, hot water, and cooling load. Through in-depth analysis from the energy, exergy, and exergo-economic perspectives, it is found that the system improves energy efficiency by about 10%. By applying the multi-objective multi-verse optimizer (MOMVO) algorithm, the second law efficiency of the system can be increased by about 9% and the product cost rate reduced by around 6% compared to the base case.
APPLIED THERMAL ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Ahmad Nekooei-Joghdani, Faramarz Safi-Esfahani
Summary: In this research, a new cloud computing scheduling algorithm MTOA-GOMVO is proposed, which combines the GOMVO and MTO algorithms to optimize task scheduling, improving execution time, response time, throughput, and SLA performance. Simulation results using NASA-iPSC real dataset show that MTOA-GOMVO outperforms baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Emre Celik, Mohamed A. Mahdy, Rania M. Ghoniem
Summary: This paper proposes a self-adaptive Equilibrium Optimizer (self-EO) to address global, combinatorial, engineering, and multi-objective optimization problems. By integrating four effective exploring phases, the new self-EO algorithm overcomes the potential shortcomings of the original EO and achieves better results compared to other nine metaheuristic algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Wangying Xu, Xiaobing Yu
Summary: A multi-objective Multi-Verse Optimization algorithm based on Gridded Knee Points and Plane Measurement technique (GKPPM-MVO) is proposed to reduce the pollution problem of fossil fuel power plants. Knee points and maximum plane distance points are applied in the local search phase to exploit and inherit information. The GKPPM-MVO algorithm shows good convergence performance, high stability, and high uniformity of the Pareto Front.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Seyed Mohammad Mirjalili, Seyedeh Zahra Mirjalili
NEURAL COMPUTING & APPLICATIONS
(2017)
Article
Optics
Mohammad Javad Safdari, Seyed Mohammad Mirjalili, Pablo Bianucci, Xiupu Zhang
Article
Engineering, Electrical & Electronic
H. Mellah, S. M. Mirjalili, X. Zhang
ELECTRONICS LETTERS
(2018)
Article
Engineering, Electrical & Electronic
Milad Zoghi, Arash Yazdanpanah Goharrizi, Seyed Mohammad Mirjalili, M. Z. Kabir
SEMICONDUCTOR SCIENCE AND TECHNOLOGY
(2018)
Article
Engineering, Electrical & Electronic
Kamyar Rashidi, Seyed Mohammad Mirjalili, Hussein Taleb, Davood Fathi
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2018)
Article
Computer Science, Information Systems
Behnaz Merikhi, Seyed Mohammad Mirjalili, Milad Zoghi, Seyedeh Zahra Mirjalili, Seyedali Mirjalili
PHOTONIC NETWORK COMMUNICATIONS
(2019)
Article
Optics
Seyed Mohammad Mirjalili, Hussein Taleb, M. Z. Kabir, Pablo Bianucci
Article
Computer Science, Artificial Intelligence
Jingwei Too, Ali Safaa Sadiq, Seyed Mohammad Mirjalili
Summary: This paper proposes a novel conditional opposition-based particle swarm optimization algorithm for feature selection. By introducing opposition-based learning and conditional strategy, the performance of the particle swarm optimization algorithm is improved. Experimental results demonstrate that the proposed approach not only achieves high prediction accuracy but also yields small feature sizes.
CONNECTION SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Weiguo Zhao, Zhenxing Zhang, Seyedali Mirjalili, Liying Wang, Nima Khodadadi, Seyed Mohammad Mirjalili
Summary: The multi-objective Artificial hummingbird algorithm (MOAHA) is developed to solve complex multi-objective optimization problems, including engineering design problems. The algorithm utilizes an external archive to save Pareto optimal solutions and maintains population diversity through a dynamic elimination-based crowding distance (DECD) method. Additionally, a non-dominated sorting strategy is merged with MOAHA to improve the convergence of the algorithm. The comprehensive tests demonstrate the superior performance of MOAHA over competitors in terms of convergence, diversity, and solution distribution. The algorithm is also shown to excel in handling challenging real-world multi-objective problems with unknown true Pareto optimal solutions and fronts.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Optics
Seyyed Mohammad Ghasem Mousavi-Kiasari, Kamyar Rashidi, Davood Fathi, Hussein Taleb, Seyed Mohammad Mirjalili, Vahid Faramarzi
Summary: This paper presents optimal graphene-based multilayer SPR biosensors for highly sensitive detection of biomolecules. The biosensor structures are optimized using a multi-objective gray wolf optimizer, aiming to maximize sensitivity and minimize FWHM. The optimized structures exhibit high sensitivity, low FWHM, and are easy to implement. The results pave the way for the development of highly-sensitive SPR biosensors.
Proceedings Paper
Computer Science, Artificial Intelligence
Seyedeh Zahra Mirjalili, Shelda Sajeev, Ratna Saha, Nima Khodadadi, Seyed Mohammad Mirjalili, Seyedali Mirjalili
Summary: Evolutionary algorithms are widely used in science and industry for optimization due to their black box nature and ability to avoid local optima. This study integrates Evolutionary Population Dynamics (EPD) into the Harmony Search (HS) algorithm and finds that optimizing 10% of the population significantly improves the algorithm's performance in simulating real-world optimization problems.
PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS (ICHSA 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Nima Khodadadi, Seyed Mohammad Mirjalili, Seyedeh Zahra Mirjalili, Seyedali Mirjalili
Summary: This paper addresses the challenges in optimizing engineering problems, such as a large number of decision variables and conflicting objectives. It proposes a chaotic version of the CSPO algorithm and demonstrates its merits through a comparative study with other meta-heuristics.
PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS (ICHSA 2022)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Sare Vatani, Hussein Taleb, Seyed Mohammad Mirjalili, Mohammad Kazem Moravvej-Farshi
2020 28TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE)
(2020)
Article
Optics
Seyed Mohammad Mirjalili, Behnaz Merikhi, Seyedeh Zahra Mirjalili, Milad Zoghi, Seyedali Mirjalili
Article
Computer Science, Interdisciplinary Applications
Seyedali Mirjalili, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad Saremi, Hossam Faris, Seyed Mohammad Mirjalili
ADVANCES IN ENGINEERING SOFTWARE
(2017)
