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imperialist competitive algorithm optimized cascade controller for load frequency control of multi-microgrid system

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 5538-5560页

作者： Nayak, Pratap Chandra Prusty, Umesh Chandra Prusty, Ramesh Chandra Panda, Sidhartha VSSUT Dept Elect Engn Burla India

Control of multi-energy source-based microgrid is a complex task due to the presence of unpredictable renewable energy sources (RES). This paper proposes a cascade structure of proportional derivative with (1+ PI) controller (C-PDF(1+ PI)) for load frequency control of an AC multi Micro-grid (MG). The MG contains a thermal power unit, Diesel Engine Generator (DEG), Micro Turbine (MT), Fuel Cell (FC), and uncontrolled solar and wind sources. Diverse storage device units like Battery Energy Storage System (BESS), Fly Wheel Energy Storage System (FESS), and Electric Vehicles (EV) are also included in the studied MG system. The proposed C-PDF(1+ PI) controller consists of a first stage Proportional Derivative controller with filter (PDF) and one plus proportional-integral (PI) controller in the second stage. The imperialist competitive algorithm (ICA) is applied to optimize the controller parameters. To confer superiority of the C-PDF(1+ PI) controller, its responses are related with PI, PID, and PIDF controllers. It is observed that the ITAE improvement in percentage with the proposed approach is 70.64%, 53.23%, and 47.10% compared to PI, PID, and PIDF controller while considering the simultaneous variations in load, solar, and wind powers. To justify the efficacy of the ICA technique, it is compared with other well-established techniques like the Genetic algorithm (GA) and the Cuckoo Search algorithm (CSA). It is observed that for the identical system and C-PDF(1+ PI) controller, the ITAE value improvement in percentage by ICA compared to GA and CSA are 31.94% and 15.93%, respectively, while considering all the three uncertainty like changes in load condition, change in solar power irradiation and changes in wind power conditions. Finally, it is conferred that the proposed ICA optimized C-PDF(1+ PI) controller significantly improves microgrid dynamic performance under various operational conditions.

关键词： Multi-energy system AC Microgrid Frequency Control Cascade Controller imperialist competitive algorithm

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imperialist competitive algorithm for unrelated parallel machine scheduling with sequence-and-machine-dependent setups and compatibility and workload constraints

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Elyasi, Milad Selcuk, Yagmur Selenay Ozener, O. Orsan Coban, Elvin Dept Logist & Operat Management HEC Montreal Montreal PQ Canada Ozyegin Univ Ind Engn Dept Istanbul Turkiye

In this paper, we present an in-depth analysis of the unrelated parallel machine scheduling problem (UPMSP) in the context of washing machine production at Vestel Electronics. Vestel Electronics is a leading producer of washing machines and holds a noteworthy market position in the European consumer electronics industry. The production process at Vestel Electronics is make-to-order (MTO) and requires 20 assembly lines to produce 200 different products. The decision maker must consider several important factors while forming the production schedule, including job-assembly line compatibility, the release times and due dates of the jobs, and a workload balance among the different assembly lines. This study aims to develop an algorithm to minimize the total earliness and tardiness while considering sequence-and-machine-dependent setups, unequal release times, machine -job compatibility restrictions, and workload balance requirements. To address this complex scheduling problem, we propose a novel algorithm, the imperialist competitive algorithm (ICA), which has a strong exploration-exploitation balance while mimicking real-world dynamics. Our numerical results show that the ICA significantly outperforms the current practice in Vestel by 39% and the best-performing algorithms in the literature by 23% and 12%.

关键词： imperialist competitive algorithm Unrelated parallel machine scheduling Machine workload Machine-job compatibility Sequence-and-machine-dependent setup times

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imperialist competitive algorithm for subcontractor selection in multiple project environments

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SOFT COMPUTING 2024年第3期28卷 1851-1864页

作者： Afshar, Mohammad Reza Zavari, Masoud Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Polytech 350 Hafez AveValiasr Sq Tehran Iran

The selection of subcontractors is a significant aspect of any project since it can have a meaningful impact on the success of the project. In other words, choosing the wrong subcontractor can lead to delays and cost overruns. Thus, selecting the best subcontractors is crucial for every general contractor. In this study, imperialist competitive algorithm (ICA) is presented to solve the subcontractor selection problem (SSP) in multiple project environments by minimizing the general contractor's cost as the objective subject to resource and precedence constraints under two different circumstances. In the first circumstance, the project deadline can be postponed by paying a penalty;while the deadline cannot be deferred in the second one. The random key (RK) and the subcontractor list representation schemes are employed as encoding procedures, and the serial schedule generation scheme (SSGS) is utilized as a decoding scheme. Comparing the results of applying the presented ICA in a case study with the results obtained using an exact method and genetic algorithm (GA) validates the effectiveness of the proposed algorithm to solve SSP in multiple project environments. The outcomes demonstrate that the proposed ICA is more efficient in cases where the project deadline is restricted.

关键词： Portfolio optimization Subcontractor selection Scheduling imperialist competitive algorithm Multiple project environments

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Global buckling analysis and weight minimization of honeycomb sandwich beams under humid environments using an analytical model and imperialist competitive algorithm

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2024年第6期52卷 3404-3425页

作者： Kamarian, Saeed Song, Jung-il Changwon Natl Univ Dept Mech Engn Chang Won South Korea

Two main goals are pursued in the present work. First, the global buckling of axially loaded sandwich beams with composite face sheets and aluminum honeycomb cores under humid environments is investigated. An analytical solution with high accuracy is developed based on piecewise low-order shear deformation theory (PLSDT) to estimate the critical buckling loads. A parametric study is carried out to examine the influence of geometric parameters on the buckling loads of simply supported and clamped sandwich beams under different wet conditions. The obtained results indicate that humidity plays a significant role in the buckling behavior of honeycomb sandwich beams. As the second purpose of the present work, the weight minimization of sandwich beams is studied. The optimization variables include the face sheet thickness, core thickness, honeycomb cell size and honeycomb cell wall thickness. Moreover, different failure modes, including global buckling, face sheet dimpling, face sheet wrinkling, core shear instability, and face sheet failure are considered as design constraints. imperialist competitive algorithm (ICA), a powerful socio-politically motivated global search strategy, is applied to handle the constrained optimization problem. The optimization results show that the presence of humidity in the environment significantly changes the optimal design of sandwich beams and makes them heavier. Furthermore, it is observed that by increasing the length of the structure, the optimal design is more affected by humidity. Moreover, it is found that ICA is a very powerful optimization tool both in obtaining the best values of geometric parameters and in reducing the computational cost.

关键词： Sandwich beams humid environments global buckling analytical solution weight minimization imperialist competitive algorithm

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A Cooperated imperialist competitive algorithm for Unrelated Parallel Batch Machine Scheduling Problem

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Computers, Materials & Continua 2024年第5期79卷 1855-1874页

作者： Deming Lei Heen Li College of Automation Wuhan University of TechnologyWuhan430070China

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a *** this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM *** complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox *** minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is *** CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search *** types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively *** experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

关键词： Release time assimilation imperialist competitive algorithm batch processing machines scheduling

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Design optimization of additively manufactured sandwich beams through experimentation, machine learning, and imperialist competitive algorithm

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JOURNAL OF ENGINEERING DESIGN 2024年第3期35卷 320-337页

作者： Teimouri, Amir Alinia, Maysam Kamarian, Saeed Saber-Samandari, Saeed Li, Guoqiang Song, Jung-il Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA USA Amirkabir Univ Technol New Technol Res Ctr Tehran Iran Amirkabir Univ Composites Res Lab CRLab Tehran Iran Changwon Natl Univ Dept Mech Engn Chang Won South Korea

The primary aim of this study was to optimize 3D-printed sandwich beams incorporating auxetic cores. Four key design variables were considered: core thickness, width, and two specific geometric parameters related to the auxetic core-the cell's wall thickness-to-length ratio and the cell angle. Initially, sixteen sandwich samples, each varying in geometric parameters, were printed following Taguchi's experimental design. These samples underwent a three-point bending test, and the resulting data were used to train a deep neural network (DNN). The trained DNN proficiently predicted the bending response of the structures. Another network, using a similar architecture, was trained to precisely predict the structure's mass. These trained networks were then integrated into the imperialist competitive algorithm (ICA) to optimize the sandwich beams design for minimal mass. A contour plot was subsequently generated to visually illustrate the minimum required mass distribution for sandwich beams under various conditions and constraints, considering applied bending forces and maximum allowable deflection. The optimization results underscored the importance of fabricating and testing an adequate number of sandwich samples, aligning with the number of design parameters. The results provide a valuable reference for future research on designing advanced 3D-printed lightweight structures, including integrating machine learning techniques and metaheuristic algorithms.

关键词： 3D-printed sandwich beams auxetic cores bending test machine learning imperialist competitive algorithm

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Solving the fuzzy p-hub center problem using imperialist competitive algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第12期15卷 6163-6183页

作者： Abbasi, Mehdi Sadough, Fatemeh Mahmoudi, Amin Islamic Azad Univ Dept Ind Engn Shiraz Branch Shiraz Iran Southeast Univ Sch Civil Engn Dept Construct & Real Estate Nanjing Peoples R China

The p-hub center location tries to minimize the maximum travel times by locating p hubs and allocating other nodes to these hub nodes. There is a wide application in various fields of study for the p-hub center problems, such as telecommunication and transportation systems. In the proposed fuzzy p-hub center problem (FPHCP) by Yang et al. (Yang et al. in Appl Soft Comput 13:2624-2632, 2013), a risk aversion PHCP is considered where travel times are trapezoidal fuzzy variables and value-at-risk criterion is the objective function. In the mentioned research, the original value-at-risk PHCP has turned into two equivalent mixed-integer linear programming models, and a genetic algorithm incorporating local search (GALS) has been applied to solve the equivalent mixed-integer linear programming models. Considering the properties of FPHCPs, the imperialist competitive algorithm (ICA) incorporating edge recombination operator (ERO) and Elzinga-Hearn algorithm (EHA) is a more suitable method to solve FPHCPs proposed in the current study. The ICA, which uses assimilation and revolution operators, is an effective tool for solving hard combinational optimization problems. The ERO is applied for assimilation in the proposed ICA, an operator that creates a parent like a set of existing parents by looking at the edges. In addition, the EHA is applied for revolution in the proposed ICA, which maintains a covering circle for a subset of the points. We evaluated the performance of the exact method (EM), GALS, and ICA incorporating ERO and EHA by solving and comparing their accuracy and speed on 81 small-scale and large-scale FPHCP samples. Our experiments demonstrate that the proposed method achieves optimal solutions in solving all small-scale sample tests of FPHCPs. Also, it was faster than EM and GALS for solving these sample tests. Finding optimum solutions for large-scale FPHCP sample tests by EM was impossible due to the limitation of computational CPU time. In solving large-sca

关键词： Fuzzy p-hub center problem imperialist competitive algorithm Elzinga-Hearn algorithm Edge recombination operator

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Novel Hybrid algorithm Based on Combined Particle Swarm Optimization and imperialist competitive algorithm for Non-Convex CHPED Solution

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第1期18卷 1-13页

作者： Yang, Yuwei Gao, Jie Gu, Hai Marani, Hashem Imani Nantong Inst Technol Sch Mech Engn Nantong 226002 Peoples R China Islamic Azad Univ Sch Elect Engn Sci & Res Tehran Iran

This paper presents a novel hybrid approach by integrating the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) method to deal with the combined heat and power economic dispatch (CHPED) problem with the bounded feasible operating region. Unlike many previous methods, this approach takes the valve-point effects explicitly into account as an absolute sinusoidal term in the conventional polynomial cost function. The efficiency and feasibility of the hybrid scheme are evaluated on three small (with three different scenarios), medium and large test systems. The simulation results suggest the superior performance of the proposed hybrid algorithm in finding optimum solutions compared to other existing methods.

关键词： Combined heat and power unit Economic dispatch imperialist competitive algorithm Particle swarm optimization Valve-point effects

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A parallel multi-objective imperialist competitive algorithm to solve the load offloading problem in mobile cloud computing

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NEURAL COMPUTING & APPLICATIONS 2023年第26期35卷 18905-18932页

作者： Alipour, Sara Saadatfar, Hamid Poor, Mahdi Khazaie Islamic Azad Univ Comp Engn Dept Birjand Branch Birjand Iran Univ Birjand Dept Comp Engn Birjand Iran

Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user's mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm's execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria.

关键词： Cloud computing Mobile cloud computing Load offloading Task scheduling imperialist competitive algorithm Parallel algorithm

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An advanced intelligent MPPT control strategy based on the imperialist competitive algorithm and artificial neural networks

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EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1437-1461页

作者： Ncir, Noamane El Akchioui, Nabil Abdelmalek Essaadi Univ Fac Sci & Technol Tetouan Al Hoceima Morocco

The development of a robust optimization control technique, which can handle numerous nonlinear system problems, is one of the most challenging aspects of science production. In this study, hybrid intelligent Maximum Power Point Tracking (MPPT) control approach based on the imperialist competitive algorithm (ICA), and an adaptive Artificial Neural Network (ANN) model is proposed to solve the efficiency optimization problem of Photovoltaic (PV) systems, which are considered as one of the most demanded source energy in the world. Consequently, a comparison between various metaheuristic algorithms based ANN including, Particle Swarm Optimization, Grey Wolf Optimization, and Whale Optimization algorithm, is made for four distinct PV panel architectures to prove the effectiveness of the suggested approach in the optimization process based ICA technique, and in the training phase based three training algorithms namely, Bayesian Regularization (BR), Levenberg Marquardt (LM), and Scaled Conjugate Gradient (SCG). Accordingly, the obtained outcomes have proven that the ICA-ANN approach-based BR algorithm outperformed in three of four cases the other techniques by reaching an accuracy that can go up to 99.9994%. In the second part of this study, the evaluation of the obtained findings confirmed that our proposed model was able to track the Maximum Power Point (MPP) faster with a response time between 1.9 and 9.6 ms, and efficiency higher than 99.9652%, which is can up to 99.9984%, and it has shown excellent remarkable stability compared to the Perturb & Observe, Incremental Conductance, and the most applied metaheuristic-based MPPT techniques that we have used to conduct the optimization performance comparison.

关键词： Artificial neural network Metaheuristic algorithms imperialist competitive algorithm Optimization Training algorithms Photovoltaic system Maximum power point tracking

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