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A Greener meta-heuristics Scheduling algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

IEEE ACCESS 2021年 9卷 18373-18382页

作者： Wang, Jinglian Gong, Bin Liu, Hong Li, Shaohui Ludong Univ Dept Informat & Elect Engn Yantai 264025 Peoples R China Shandong Univ Dept Software Jinan 250101 Peoples R China Shandong Normal Univ Dept Informat Sci & Engn Jinan 250014 Peoples R China Shandong Key Lab Distributed Comp Software New Te Jinan 250014 Peoples R China

As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CA(R)_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CA(R)_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.

关键词： Scheduling Dynamic scheduling Heuristic algorithms Measurement Optimization Green products Program processors Greener energized optimization dynamics fusion intelligence meta-heuristics algorithm nonlinear heterogeneous scheduling

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Registration of Lung CT-Images Affected with Covid-19 Using Centroid Opposition-Based Multiverse Optimizer

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SN Computer Science 2025年第3期6卷 1-18页

作者： Nayak, Somen Sarkar, Achyuth Mondal, Subhodip Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote Arunachal Pradesh Itanagar 791113 India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering Subhankar Nagar Pohabagan West Bengal Bankura 722146 India

Medical image registration can be used to address a variety of practical difficulties that are related to the collection of medical pictures from multiple modalities. Multimodal registration uses intensity-based registration approaches, and these strategies will amalgamate supplementary images that possess the same content into a singular representation through image manipulation. It is crucial to optimize the modification of computed tomography (CT) scans. The improvement of the similarity metric between the various scans is really *** optimization methods have been proposed recently, focusing on the foundational elements of optimization. However, from the perspectives of efficiency and quality, there is still a lot that may be improved. For diagnosis, Lung CT image registration is crucial in identifying the spot differences between the various Lung CT images. The registration of lung CT scans using multiverse optimization (MVO) with computing opposition-based learning followed by chaotic-based initialization is suggested in this research. We have proposed the meta heuristics-based Optimization (MVO_COBL) algorithm to register lung CT images for both multimodal and monomodal medical image registration. We first collected CT scans of the lungs from the data *** are then processed using MVO_COBL, and the registered images are finally extracted. The suggested lung CT image registration method’s outcomes are contrasted with those of the hybrid particle swarm optimization with Grey wolf optimizer (HPSGWO), crow search algorithms (CSA), Grey wolf optimizer (GWO), and particle swarm optimization (PSO) based methods. The experimental results statistically speaking that, the suggested method performs better than HPSGWO, PSO, GWO and CSA methods in registering lung CT images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feature-based registration Intensity-based registration Medical image meta-heuristics algorithm Mono modality image registration Multi modality Mutual information Mutual information Similarity metric

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Artificial Orca Optimiser: Theory and Applications for Global Optimisation Problems

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EXPERT SYSTEMS 2025年第4期42卷

作者： Wang, Lin Wang, Xuerui Pi, Yingying Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China

With the growing complexity of real-world engineering optimisation problems, interest in meta-heuristic algorithms is increasing. However, existing meta-heuristic algorithms still suffer from several shortcomings, including a poor balance between global and local search, a tendency to converge toward the centre of the solution space, and susceptibility to getting trapped in local optima. To overcome these shortcomings, a novel meta-heuristic algorithm, called artificial orca optimiser (AOO), is proposed based on the unique behaviours of orcas in nature. Within the framework of AOO, the switching factor, guidance phase, and iterative formulas that do not converge toward the centre of the solution space, are designed to enhance the equilibrium between exploration and exploitation, ensure agents the ability to escape from the local optimum, and comprehensively explore the solution space without being limited to the centre of the solution space, thereby increasing the likelihood of finding the global optimal solution. Qualitative, quantitative, scalability, sensitivity, and practical application analyses of the experimental results demonstrate that AOO overcomes the issue of converging to the centre of the solution space, alleviates the problems of poor balance and susceptibility to the local optimum, and exhibits outstanding optimising performance, fast convergence, great scalability, high robustness, and excellent practicality.

关键词： artificial orca optimiser engineering design meta-heuristics algorithm real-world applications

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Synthesis of Concentric Circular Antenna Array Using Whale Optimization algorithm

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IETE JOURNAL OF RESEARCH 2023年第12期69卷 8940-8959页

作者： Kundu, Krishanu Bera, Rajesh Pathak, Narendra Nath GL Bajaj Inst Technol & Management Dept Elect & Commun Engn Greater Noida India Gayatri Vidya Parishad Coll Engn Dept Elect & Commun Engn Visakhapatnam Andhra Pradesh India Dr BC Roy Engn Coll Dept Elect & Commun Engn Durgapur India

Through the ages the importance of the swarm intelligence-based algorithm has gained popularity in the field of optimization. The Whale Optimization algorithm (WOA) is one such popular meta-heuristics nature inspired swarm intelligence-based algorithm which is well accepted for solving optimization problems, as it is simple and capable to obtain optimal results efficiently. In this paper, few benchmark functions along with radiation patterns of concentric circular antenna array are presented and their performance is optimized using WOA. Simulation results are shown by taking various benchmark functions, namely: Three hump & Six hump camel function, Ackley, Egg Crate Function, and Egg Holder Function. The capability of the WOA algorithm is also tested through pattern synthesis with respect to the thinned array as well as non-uniformly excited array for four diverse sets of CCAA geometries. When comparison is being done with respect to renowned and most popular optimization approaches like PSO, the WOA technique proves to be excellent in terms of maximum SLL suppression. Optimization results show the better capability of WOA in the field of optimization.

关键词： Benchmark functions Concentric Circular Antenna Array (CCAA) meta-heuristics algorithm optimization Side Lobe Level (SLL) Whale Optimization algorithm (WOA)

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Principle and performance validation of search and rescue team algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第12期38卷 6701-6717页

作者： Tong, Chengbiao Sepehri, Nariman Hunan Agr Univ Coll Elect & Mech Changsha 410128 Peoples R China Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada

Combinatorial optimization problems exist in various fields. However, their solution is uncertain, particularly for high-dimensional non-deterministic polynomial-time hard (NP-hard) problems. To achieve a more stable solution for high-dimensional problems, a new metaheuristics algorithm, called the search and rescue team (SaRT) algorithm, is proposed herein. This article presents its mathematical model and verification process using fifteen test case studies including five knapsack problems and ten multi-modal test functions, also taking optimization of the crank slider mechanism as an engineering application case. The parameter sensitivity of the SaRT was studied and its output was found to be insensitive to the parameters. The SaRT can converge to the global optimum with a probability of more than 96 % for NP-hard problems, and also shows excellent performance and robustness in multi-modal test functions and multi-objective optimization compared with other popular algorithms. Hence, the proposed algorithm can be employed for optimization design and parameter tuning in engineering control and fault diagnosis.

关键词： meta-heuristics algorithm Multi-modal test function Knapsack problem Nelder-mead (NM) algorithm Non-deterministic polynomial-time hard problem

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Modeling and Optimal Design of a Short Primary Double-Sided Linear Induction Motor Considering Both Transverse and Longitudinal End Effects Using Improved Differential Evolution

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第2期18卷 1065-1081页

作者： Wang, Hanming Zhao, Jinghong Guo, Guoqiang Chen, Hansi Xiong, Yiyong Yan, Sinian Naval Univ Engn Dept Elect Engn Wuhan Peoples R China

Due to its simple structure, the linear induction motor provides a linear driving force without any intermediate conversion devices. Linear induction motors are gradually used in various applications, including rail transit and the national defense industry. However, linear induction motors are affected by the end-effect phenomenon and suffer from problems such as low efficiencies and low power factors. In this paper, to optimize the performance of short primary double-sided linear induction motors (SP-DLIMs), first, a new analytical model is established based on an equivalent circuit considering both transverse and longitudinal end effects. Second, to analyze the impact of main parameters on the output characteristics of SP-DLIMs, a simple iterative design method (IDM) is presented. Third, a multi-objective optimal design method is proposed to optimize the efficiency, power factor and tooth weight of SP-DLIMs, with proposed design constraints and four different optimization problems. Moreover, an improved differential evolutionary (IDE) algorithm is proposed to optimize the design. A comparison of the results proved that the IDE yielded a competitive performance to those of other advanced heuristic algorithms. Finally, a three-dimensional time-stepping finite-element method was utilized to verify the validity of the optimization results.

关键词： Artificial intelligence Double-sided linear induction motor Design optimization Electrical engineering meta-heuristics algorithm

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TSMGWO: Optimizing Task Schedule Using Multi-Objectives Grey Wolf Optimizer for Cloud Data Centers

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IEEE ACCESS 2021年 9卷 37707-37725页

作者： Alsadie, Deafallah Umm Al Qura Univ Dept Informat Syst Mecca 24381 Saudi Arabia

Cloud computing is considered to be the best solution for addressing the increasing computing requirements of high-performance applications. The efficient performance of the system requires optimal mapping of cloud tasks to resources. However, it is challenging to address computing and storage requirements of high-performance applications while achieving conflicting scheduling objectives like throughput, makespan, resource utilization. This work proposes a metaheuristic approach called task schedule using a multi-objective grey wolf optimizer (TSMGWO) to find near-optimal task scheduling solutions while handling conflicting objectives. The TSMGWO approach has been evaluated using three benchmark datasets, namely, GoCJ, HCSP and Synthetic dataset. The results are compared with heuristic FCFS and MT methods, and metaheuristic methods PSO, GA and WOA. The TSMGWO approach reduces makespan upto 67.52% over FCFS method, 60.93%, over PSO, 38.05% over GA, and 23.22% over WOA methods for 100 tasked cloud workload using GoCJ dataset. It reduces makespan upto 60.95% over FCFS method, 55.79% over MT method, 47.04% over PSO, 33.38% over GA and 19.91% over WOA method using synthetic dataset. Similarly, TSMGWO reduces makespan upto 27.03% over MT method, 18.95% over PSO, 11.90% over GA and 7.5% over WOA method using HSCP workload. The comparative analysis demonstrates that TSMGWO approach outperforms the earlier heuristic and metaheuristic methods using benchmark datasets in the cloud environment.

关键词： Task analysis Cloud computing Processor scheduling Scheduling Resource management Optimal scheduling Throughput Optimization cloud computing task scheduling meta-heuristics algorithm multi-objective grey wolf optimizer

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A metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

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IEEE ACCESS 2021年 9卷 74218-74233页

作者： Alsadie, Deafallah Umm Al Qura Univ Dept Informat Syst Mecca 24381 Saudi Arabia

Optimal allocation of virtual machines in a cloud computing environment for user-submitted tasks is a challenging task. Finding an optimal task scheduling solution is considered as NP-hard problem specifically for large task sizes in the cloud environment. The best solution involves scheduling the tasks to virtual machines data centre while minimizing the essential, influential and cost effective parameters such as energy usage, makespan and cost. In this direction, this work presents a metaheuristic framework called MDVMA for dynamic virtual machine allocation with optimized task scheduling in a cloud computing environment. The MDVMA focuses on developing a multi-objective scheduling method using non dominated sorting genetic algorithm (NSGA)-II algorithm-based metaheuristic algorithm for optimizing task scheduling with the aim of minimizing energy usage, makespan and cost simultaneously to provide trade-off to the cloud service providers as per their requirements. To evaluate the performance of the MDVMA approach, we compared the performances of two different scenarios of benchmark real-world workload data sets using the existing approaches, namely, Artificial Bee Colony (ABC) algorithm, Whale Optimization algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm. Simulation results demonstrate that optimizing task scheduling leads to better overall results in terms of minimizing energy usage, makespan and cost of the cloud data center. Finally, the paper concludes metaheuristic algorithms as a promising method for task scheduling in a cloud computing environment.

关键词： Task analysis Cloud computing Scheduling Processor scheduling Heuristic algorithms Data centers Virtual machining Cloud computing energy consumption task scheduling meta-heuristics algorithm optimization

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Non-convex Dynamic Economic Dispatch Using an Innovative Hybrid algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第2期17卷 863-902页

作者： Verma, Pooja Parouha, Raghav Prasad Indira Gandhi Natl Tribal Univ Dept Math Amarkantak 484887 MP India

An innovative hybrid algorithm (ihPSODE) is proposed in this paper to solve non-convex dynamic economic dispatch (DED) problem. It integrated with a novel particle swarm optimization (nPSO) and differential evolution (nDE). A new inertia weight, gradually varying acceleration coefficient and position update equation introduced in nPSO, to avoid stagnation. And in nDE a new mutation strategy and crossover rate are presented, to avoid premature convergence. Additionally, in ihPSODE after calculation identify best half member and discard rest of members from the population. In current population applied nPSO, to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applied nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly. Firstly, proposed ihPSODE as well as its anticipated integrating component nPSO and nDE are testified on IEEE CEC2006 constrained benchmark functions. Then performance of these proposed algorithms are evaluated with non-convex DED problem with valve-point effects using five different test systems (5-unit with losses, 10-unit without losses, 10-unit with losses, 15-unit with losses and 30-unit without losses). Several numerical and graphical experiments have been done to verify performances of proposed algorithms. Additionally, statistical and comparative analysis confirms superiority of proposed algorithms over many state-of-the-art algorithms.

关键词： Dynamic economic dispatch Constrained optimization meta-heuristics algorithm Hybrid algorithm

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Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution

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KNOWLEDGE-BASED SYSTEMS 2019年 169卷 39-52页

作者： Abd Elaziz, Mohamed Xiong, Shengwu Jayasena, K. P. N. Li, Lin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Balangoda Sri Lanka Zagazig Univ Fac Sci Dept Math Zagazig Egypt

This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The proposed method is based on the improvement of the Moth Search algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Task scheduling Cloud computing Makespan Moth search algorithm (MSA) Differential evolution (DE) meta-heuristics algorithm

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