Article
Computer Science, Artificial Intelligence
Xin-rui Tao, Jun-qing Li, Ti-hao Huang, Peng Duan
Summary: The research on resource-constrained hybrid flowshop problem led to the proposal of a discrete imperialist competitive algorithm (DICA) to minimize makespan and energy consumption. The algorithm represents solutions using two-dimensional vectors, with one for scheduling sequence and the other for machine assignment, and incorporates a decoding method considering resource allocation. By combining DICA with simulated annealing algorithm (SA), the proposed approach showed high efficiency in solving the RCHFS problem.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Maryam Houtinezhad, Hamid Reza Ghaffary
Summary: The goal of optimizing the best acceptable answer is to define target functions and select optimal answers based on the limitations and needs of the problem. When dealing with multi-objective issues, a new objective function can be formed by a linear combination of the main objective functions. The combination of particle swarm optimization with the Imperialist Competitive Algorithm aims to increase the ability to discover new positions. By measuring the cosine similarity of neighboring countries and balancing global and local search, the performance of the two algorithms is improved.
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
(2021)
Article
Energy & Fuels
Majid Khanali, Asadollah Akram, Javad Behzadi, Fatemeh Mostashari-Rad, Zahra Saber, Kwok-wing Chau, Ashkan Nabavi-Pelesaraei
Summary: The study focuses on the energy flow and environmental emissions of walnut orchards in Alborz province of Iran, aiming to optimize them through a multi-objective imperialist competitive algorithm. Results show energy inefficiency in walnut production, with gasoline being the dominant energy consumer. Environmental findings indicate on-orchard emissions and gasoline as the main hotspots.
Article
Geochemistry & Geophysics
Amir Joolaei, Alireza Arab-Amiri, Ali Nejati
Summary: Traditionally, local deterministic optimization techniques have been used for nonlinear gravity inversion problems, but recently global optimization methods such as a hybrid of ICA and FA algorithm have shown promising results. This hybrid method improves exploratory capability and convergence rate, making it a potential alternative to local optimization techniques in highly nonlinear geophysical problems.
Article
Computer Science, Interdisciplinary Applications
A. Kaveh, P. Rahmani, A. Dadras Eslamlou
Summary: This paper introduces a new hybrid algorithm ICHHO, combining HHO and ICA, which successfully improves the search strategy and demonstrates competitive performance through comparisons with other techniques and problems.
ENGINEERING WITH COMPUTERS
(2022)
Article
Construction & Building Technology
Jianyang Cai, Haidong Yang, Tiancheng Lai, Kangkang Xu
Summary: A new optimization algorithm based on an improved imperialist competitive algorithm (ICA-DE) is proposed to reduce the energy consumption of a multi-chiller system. The idea of differential mutation proposed by differential evolution (DE) was applied to create more new locations for colonies and increase population diversity in the assimilation process of ICA. The ICA-DE method was used to distribute the partial load rate (PLR) of chillers and achieved good results in reducing energy consumption.
ENERGY AND BUILDINGS
(2023)
Article
Mathematics
Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu
Summary: This paper introduces a novel neural network optimization method that combines improved evolutionary competitive algorithm and gradient-based backpropagation. By incorporating backpropagation and self-adaptive hyperparameter adjustment strategy, this method generates regression models that are better correlated with the desired outputs and provides more accurate predictions.
Article
Computer Science, Artificial Intelligence
Xia Li, Junhan Chen, Lingfang Sun, Jing Li
Summary: Intelligent optimization algorithms play an important role in solving global optimization problems. The imperialist competitive algorithm (ICA), a nature-inspired meta-heuristic algorithm, tends to fall into local optima. To address this issue, an improved ICA algorithm is proposed, which incorporates the theory of spiral rising to expand search space and enhance global search ability.
PEERJ COMPUTER SCIENCE
(2022)
Article
Telecommunications
E. Shafiee, M. R. Mosavi, M. Moazedi
Summary: This paper presents an intelligent dynamic algorithm-based GPS spoofing attack detection method that detects spoofing behavior by distinguishing correlation peaks of authentic and counterfeit signals, and validations show that it can successfully detect spoofing attacks in 99.7% of cases.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Egon Henrique Salerno Galembeck, Salvador Pinillos Gimenez, Rodrigo Alves de Lima Moreto
Summary: The design and optimization of analog CMOS ICs are complex and dependent on designers' experience, with long design cycle times. This study presents the first use of a custom imperialist competitive algorithm (ICA) to reduce the design cycle times of analog CMOS ICs. The results show that using an ICA-customized evolutionary algorithm can reduce the design cycle times by up to 83% compared to using a GA-customized evolutionary algorithm.
Article
Computer Science, Information Systems
Keyvan Golalipour
Summary: With the widespread use of smartphones and mobile internet, there has been explosive growth in the sharing of images on social media networks. This paper proposes a hybrid model that combines a novel permutation technique with a diffusion method based on a chaotic function. The results show that the proposed method has excellent resistance against brute force and statistical attacks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Gilberto Rivera, Raul Porras, J. Patricia Sanchez-Solis, Rogelio Florencia, Vicente Garcia
Summary: This paper introduces a novel metaheuristic called Outranking-based Particle Swarm Optimization (O-PSO) for addressing the multi-objective Unrelated Parallel Machine Scheduling Problem. O-PSO is an optimization algorithm that combines particle swarm optimization with the preferences of the Decision Maker (DM) expressed in a fuzzy relational system based on ELECTRE III. Unlike other multi-objective metaheuristics, O-PSO focuses on finding the Region of Interest (RoI) instead of approximating a sample of the complete Pareto frontier. The efficiency of O-PSO is validated through experiments on synthetic instances and a real-world case study, showing its capability of generating high-quality solutions and supporting multicriteria decision analysis.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Biology
Mehdi Ayar, Ayaz Isazadeh, Farhad Soleimanian Gharehchopogh, MirHojjat Seyedi
Summary: This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) for optimal feature selection. The NSICA is a modified version of the original Imperialist Competitive Algorithm (ICA) that solves optimization problems through competition between colonies and imperialists. By modifying the operations and using a non-dominated sorting approach, this study addresses challenges such as discretization and elitism. The proposed algorithm is application-independent and can be customized for any feature selection problem. Evaluation results show the efficiency of NSICA compared to other state-of-the-art algorithms, using it as a feature selection system for diagnosing cardiac arrhythmias.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Babak Rezaei, Frederico Gadelha Guimaraes, Rasul Enayatifar, Pauline C. Haddow
Summary: This article introduces a hybrid metaheuristic algorithm, ICAHGS, for solving the Capacitated Vehicle Routing Problem (CVRP). The algorithm combines the refined Imperialist Competitive Algorithm (ICA) and the Hybrid Genetic Search (HGS-CVRP) algorithm, with a multi-step restart mechanism for intensification improvement. The proposed method allows for parallel processing, resulting in increased computational efficiency. Comparative experiments demonstrate the competitive performance of the proposed algorithm.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Reza Afshar, Masoud Zavari
Summary: The selection of subcontractors is crucial for the success of a project as it can significantly impact the project's outcomes. Choosing the wrong subcontractor can result in delays and cost overruns, highlighting the importance of selecting the best subcontractor.
Article
Computer Science, Artificial Intelligence
Fatemeh Pourdehghan Golneshini, Hamed Fazlollahtabar
Article
Robotics
Hamed Fazlollahtabar
Article
Computer Science, Artificial Intelligence
Hany Seidgar, Hamed Fazlollahtabar, Mostafa Zandieh
Article
Computer Science, Interdisciplinary Applications
Hamed Fazlollahtabar, Hadi Gholizadeh
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Hadi Gholizadeh, Nikbakhsh Javadian, Hamed Fazlollahtabar
Article
Green & Sustainable Science & Technology
Hadi Gholizadeh, Hamed Fazlollahtabar, Mohammad Khalilzadeh
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Computer Science, Interdisciplinary Applications
Hadi Gholizadeh, Hamed Fazlollahtabar
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Article
Robotics
Hamed Fazlollahtabar
Summary: Industry 4.0 integrated with robotic and digital fabrication technologies has attracted the attention of manufacturing researchers. This paper proposes an intelligent control system based on SCADA in the IoT platform for processing configuration and reconfiguration of an autonomous assembly system, and the implementation study confirms its effectiveness.
Article
Engineering, Industrial
Samane Babaeimorad, Parviz Fattahi, Hamed Fazlollahtabar
Summary: The paper presents an integrated strategy for inventory control and preventive maintenance planning for a single-machine production system. By using a numerical algorithm to find the optimal policy, it reduces production system costs and effectively deals with customer loss.
JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING
(2022)
Article
Green & Sustainable Science & Technology
Zahra Taherikhonakdar, Hamed Fazlollahtabar
Summary: These days software plays an important role in various aspects of our lives. With the increasing use of computers, mobile applications, and embedded systems, the energy consumption of software has become a growing concern. Green IT has emerged as a focus on optimizing software solutions to reduce energy consumption. Despite the importance of green software development, few developers pay attention to software energy consumption, and even fewer users care about the energy consumption of the software they use. This article aims to help software developers develop energy-efficient software and inform users about the energy consumption of the software they use in order to ultimately reduce the negative impact of software on the environment.
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2023)
Article
Computer Science, Software Engineering
Hamed Fazlollahtabar
Summary: Supplier selection is a significant problem in supply chain management, involving concurrent decision-making on key performance indicators based on multi-dimensional data. Recent studies have examined the supplier selection problem in various applied cases, with a focus on a range of criteria. This problem becomes more pronounced in industries with high levels of investment, such as the renewable energy sector. This article presents a new method that encompasses all relevant indices for effective supplier selection. The proposed algorithm utilizes decision tree (DT) indices to group criteria and sub-criteria, and uses a machine learning (RML) approach to handle uncertain data through rough comparisons and weighing. The method also includes a transformation (T) step to obtain crisp values and a ranking (R) of suppliers. A case study on renewable energy supplier selection demonstrates the effectiveness of the proposed method, particularly in handling big data through machine learning techniques. The article also discusses the managerial implications of this method as decision support.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Business
Nasim Ganjavi, Hamed Fazlollahtabar
Summary: In today's competitive environment, quality management is crucial for companies to reach a larger market share and economic success. The integration of physical machinery systems with digital networking in Industry 4.0 provides extensive opportunities for quality-related issues. To encompass all dimensions effective on quality management, it is necessary to process a large amount of data within the context of Industry 4.0. Advanced production systems and quality management are complementary resources to enhance functionality and gain a higher competitive advantage.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Engineering, Multidisciplinary
S. Babaeimorad, P. Fattahi, H. Fazlollahtabar
Summary: Machine maintenance in production is crucial for preventing machine failure and maintaining production efficiency. An integrated schedule for production and maintenance is necessary. This paper discusses a parallel machine scheduling problem with individual maintenance operations, formulates a mathematical model, and proposes a branch and bound algorithm for optimization. The results demonstrate the effectiveness of the mathematical model and the efficiency of the adapted algorithm in comparison with Gams optimization software.
INTERNATIONAL JOURNAL OF ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Behnam Rahimikelarijani, Hamed Fazlollahtabar, Sina Nayeri
SN APPLIED SCIENCES
(2020)
Article
Engineering, Multidisciplinary
R. Shafaei, A. Mozdgir