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metaheuristic algorithm-Based Vibration Response Model for a Gas Microturbine

SENSORS 2022年第12期22卷 4317-4317页

作者： Montoya-Santiyanes, L. A. Rodriguez-Abreo, Omar Rodriguez, Eloy E. Rodriguez-Resendiz, Juvenal Univ Politecn Queretaro Ind Technol Div Carretera Estatal 420 El Marques 76240 Mexico Red Invest OAC Optimizac Automatizac & Control El Marques 76240 Mexico Ctr Ingn & Desarrollo Ind CIDESI Queretaro 76125 Mexico Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 degrees C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

关键词： microturbine vibration analysis metaheuristic algorithm response surface

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A New metaheuristic algorithm Based on Shark Smell Optimization

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COMPLEXITY 2016年第5期21卷 97-116页

作者： Abedinia, Oveis Amjady, Nima Ghasemi, Ali Semnan Univ Dept Elect Engn Semnan Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real-world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm. (C) 2014 Wiley Periodicals, Inc.

关键词： shark smell optimization metaheuristic algorithm optimization problem

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metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks

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MICROPROCESSORS AND MICROSYSTEMS 2023年第1期101卷

作者： Prakash, Ved Pandey, Suman AKTU KNIT Sultanpur Lucknow India

The utilization of wireless sensor networks (WSNs), which combine sensing, communication, and computation in an intelligent environment. The use of energy becomes the primary obstacle for sensor nodes to overcome. Clustering and routing algorithms are promising ways to fix the issue and prolong the network's effectiveness. The availability of restricted energy resources is the most significant challenge in WSNs. In this paper, we propose an energy-efficient clustering scheme integrated with a particle swarm optimization method called PSO-EECS for wireless sensor networks to raise energy efficiency while simultaneously increasing the lifetime of sensor nodes. The predominant concern being addressed in this paper is the selection of a Cluster Head (CH), which helps gather, aggregate and forward the data from the cluster-based routing paradigm. PSO-EECS algo-rithm is used to optimize the fitness parameters for CH selection, which include the energy ratio, distance considerations, node density, load balancing, and the Network's average energy, among other things. The simulation results reveal that the proposed technique offers a considerable improvement in network stability and operation time compared to the currently used approaches. Compared to the PSOGSA, GAPSO-H, and ABE-DE protocol, simulation findings demonstrate that PSO-EECS enhances stability by 95.4%, 34.8%, and 29.2%, respectively.

关键词： WSNs PSO-EECS Clustering scheme metaheuristic algorithm Cluster head

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A new improved Newton metaheuristic algorithm for solving mathematical and structural optimization problems

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EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2749-2789页

作者： Amiri, Ahmad Torkzadeh, Peyman Salajegheh, Eysa Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

As the objective function in complex structural optimization problems is implicit, in the present improved Newton method using the higher-order approximation, a new and simple formulation is proposed to calculate the first and second-order derivatives of the objective function. Furthermore, by combining the presented improved Newton method with an innovative metaheuristic algorithm an efficient approach, called improved Newton metaheuristic algorithm (INMA), is presented to balance between exploration and exploitation during optimization process. The performance of the proposed INMA algorithm is investigated on the CEC2019 and CEC2020 benchmark functions, three benchmark truss problems with stress constraints as well as four truss problems with multiple frequency constraints. The results demonstrate that the efficiency of the proposed INMA algorithm for both mathematical and structural optimization problems is better than other algorithms. Anyway, it can be seen from the results that INMA algorithm is an efficient method to solve problems in different optimization domains.

关键词： metaheuristic algorithm Improved Newton method Structural optimization Truss problem

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Application of metaheuristic algorithm in intelligent logistics scheduling and environmental sustainability

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 1727-1740页

作者： Jin, Xin Kunming Univ Sch Econ & Management Kunming Yunnan Peoples R China

In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the metaheuristic algorithm. This article experimentally compared the effects of different metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability.

关键词： metaheuristic algorithm intelligent logistics scheduling environmental sustainability path optimization performance test

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Literature Research Optimizer: A New Human-Based metaheuristic algorithm for Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12817-12865页

作者： Ni, Lei Ping, Yan Yao, Na Jiao, Jiao Wang, Geng Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Tianfu Inst Res & Innovat Chengdu 610299 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China

Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems.

关键词： Literature research optimizer metaheuristic algorithm Human-based Optimization Real-world problems

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Ebola Optimization Search algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Ebola Optimization Search Algorithm: A new nature-inspired m...

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IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Univ KwaZulu Natal Sch Math Stat & Comp Sci Pietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

ISBN: (纸本)9781665442312

Deriving computational solutions to address complex life problems is now a gaining research focus. This is because such a computational approach is capable of modeling algorithms after natural phenomenon to find an optimal solution. In this paper, a nature-inspired and biology-based algorithm was inspired by the natural and biological process of Ebola. We formulated the propagation of the disease using a mathematical and SIR-model. Thereafter, an algorithmic design describing the procedure for the optimization process was done. The resulting Ebola optimization search algorithm (EOSA) was evaluated using the popular IEEE CEC functions' benchmark functions. The outcome of exhaustive experimentation with EOSA showed that the metaheuristic algorithm achieved a state-of-the-art performance compared with similar algorithms. Results confirmed that the scalability analysis, convergence analysis, and sensitivity analysis were competitive when compared with evolutionary-based and swarm-based algorithms.

关键词： complex problems metaheuristic algorithm optimization problems benchmark functions Ebola virus

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Solving Resource-Constrained Project Scheduling Problem Using metaheuristic algorithm 5

Solving Resource-Constrained Project Scheduling Problem Usin...

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5th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Munlin, Mudarmeen Mahanakorn Univ Technol Fac Informat Sci & Technol Bangkok Thailand

ISBN: (纸本)9781538663929

We propose the metaheuristic algorithm to solve the The Resource-Constrained Project Scheduling Problem (RCPSP). The approach method extends the Particle Swarm Optimization (PSO) by regrouping the agent particles within the appropriate radius of the circle. It initializes the group of particles, calculates the fitness function, and finds the best particle in that group. Then, it incorporates the adaptive mutation and forward-backward improvement to hybridize local search algorithm for constructing the feasible project scheduling with the minimal make-span. The efficiency of the proposed method is tested against the well-known benchmarks. The results show that the proposed method gives better optimum rate and standard deviation than some existing procedures.

关键词： resource constrained project scheduling problem particle swarm optimization adaptive mutation forward-backward improvement metaheuristic algorithm

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Electric machines control optimization by a novel geo-inspired earthquake metaheuristic algorithm

Electric machines control optimization by a novel geo-inspir...

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Nanotechnology for Instrumentation and Measurement Conference (NANOfIM)

作者： Mendez, E. Ortiz, A. A. Ponce, P. Molina, A. Tecnol Monterrey Sch Sci & Engn Cdmx Mexico

ISBN: (纸本)9781538691618

This paper presents a speed control of Direct Current (DC) motor with a Proportional-Integral-Derivative (PID) controller optimized by a new metaheuristic algorithm. A novel metaheuristic optimization method is proposed in this paper based on an earthquake as geology phenomenon. The Earthquake algorithm (EA) is used to optimize both plant model and PID controller. Experimental results show a feasibility of the proposed method improving both the plant model and speed controller by PID. Besides, the implemented experimental control system proves that EA works in all different continuous nonlinear functions or engineering applications.

关键词： DC motor optimization PID control metaheuristic algorithm

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SEHAS: A Novel metaheuristic algorithm for Home Appliances Scheduling in Smart Grid

SEHAS: A Novel Metaheuristic Algorithm for Home Appliances S...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Fan, Chun-Hung Chen, Huan Tsai, Chun-Wei Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9781728145693

The home appliance scheduling (HAS) problem is a critical issue in the home energy management system (HEMS). The aim of the HAS is to control the appliances at home more efficiently and economically. However, there is still plenty room for improvement because the home appliances scheduling problem is a challenging issue due to its complexity. Many researchers have proposed schemes to solve this problem, but most of the results are either unsatisfied or taking too much computation effort but with limited performance improvement. In this paper, we propose a novel metaheuristic algorithm called search economics for home appliances scheduling (SEHAS) to address this issue more efficiently. SEHAS approach is based on search economies (SE). We first formulate the the HAS problem as a knapsack problem, and then we define our objectives as minimization of the electricity cost. We then present the proposed SEHAS in details to show how it can solve the scheduling problem efficiently. To better evaluate our proposed scheme, we conduct simulations to compare results with two classical metaheuristic algorithm, including genetic algorithm (GA) and ant colony optimization (ACO) algorithms. The experimental results show that when compared to the scenario without any scheduling algorithm that GA and ACO can reduce the electricity cost 17.60% and 18.05% respectively, and the proposed metaheuristic algorithm SEHAS can save the cost up to 19.29%. In addition, GA, ACO and SEHAS can reduce peak-to-average ratio (PAR) 13.53%, 24.01% and 24.01% respectively.

关键词： metaheuristic algorithm search economics appliances scheduling model smart grid

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