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Optimal design of a honeycomb core composite sandwich panel using evolutionary optimization algorithms

COMPOSITE STRUCTURES 2016年 139卷 254-262页

作者： Gholami, Meghdad Alashti, Reza Akbari Fathi, Alireza Babol Univ Technol Dept Mech Engn Babol Sar Iran

The present paper deals with the optimal design of a composite sandwich panel with honeycomb core structure using particle swarm optimization (PSO) technique. The face sheets of sandwich panel are considered to be thin and the sandwich panel is subjected to a uniformly distributed normal load. The Navier type solution and the closed-form expressions for the macroscopic in-plane elastic constants of the honeycombs are used to predict the deflection of the panel. Also, the maximum stress in the composite sandwich panel is determined using the macroscopic stress-strain relation of the honeycomb core and the plate. Niching Memetic PSO (NMPSO) and Locally Informed Particle Swarm (LIPS) variants of PSO are examined to minimize the weight of the panel. Numerical results showed that the optimal geometry of the honeycomb cell has this property that its radius and thickness converge to their lower bounds while its length converges to its upper bound. This means that to have an optimal panel, each cell should have the allowable minimum cross section (highest number of cells) and maximum allowable length. Moreover the effects of panel aspect ratio, cell width and applied load were examined. Also, the numerical results confirm the efficiency and effectiveness of the NMPSO in finding optimal solution on the constrained and unconstrained objective functions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Honeycomb Sandwich panel Particle swarm optimization Niching Memetic PSO evolutionary optimization algorithms Locally Informed Particle Swarm (LIPS)

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Regression line shifting mechanism for analyzing evolutionary optimization algorithms

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SOFT COMPUTING 2017年第21期21卷 6237-6252页

作者： Biswas, Anupam Biswas, Bhaskar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

This work introduces a novel methodology to perform the comparative analysis of evolutionary optimization algorithms. The methodology relies simply on linear regression and quantile-quantile plots. The methodology is extrapolated as the one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, many-to-many comparison, i.e., ranking of algorithms is done only in terms of solution quality. The proposed method is capable of ranking algorithms in terms of both solution quality and convergence rate. Method is analyzed with well-established algorithms and real data obtained from 25 benchmark functions.

关键词： evolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis

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Path Planning of Drones Using Hybrid evolutionary optimization algorithms for Inventory Scanning in Warehouses

Path Planning of Drones Using Hybrid Evolutionary Optimizati...

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作者： Mahadik, Tejas Prabhakar State University of New York at Binghamton

学位级别：M.S., Master of Science/Master of Surgery

Drones, or unmanned aerial vehicles (UAV), can significantly reduce the cost and time required for the inventory counting that takes place daily, weekly, or monthly to maintain the record of the current inventory levels. This thesis introduces an approach for finding the optimal drone path planning in the dynamic warehouse environment. This study aims to minimize energy consumption and simultaneously scan the inventory levels. Earlier, various approaches are proposed, which use multiple sensors (e.g., GPS, IMU) to increase the efficiency of localization and mapping of drones but have been proved to be ineffective in indoor environments. A 3-D grid cell representation of the static environment is carried out with each cell assigned to an ArUco marker. These ArUco markers consist of real-world coordinates that can be captured with the primary cameras present on the drone. Various state-of-the-art evolutionary optimization algorithms (EOA) such as Simulated Annealing (SA), Ant Colony optimization (ACO), and Particle Swarm optimization (PSO) and unsupervised machine learning techniques like Self-Organizing Maps (SOM) are used, and a hybrid version of each of them is developed to obtain the set of non-dominated solutions, which are found by searching the available neighborhood cells. The results show the effectiveness of applied hybrid algorithms in solving the various test cases and the SOM-PSO version produces better results when compared to SOM-ACO and SOM-SA versions. It was observed that SOM-PSO was better than the other two hybrid algorithms and produced results with an average of 1.89% optimality gap for the benchmark TSP dataset and an average of 6.73% for the warehouse test case dataset.

关键词： Drones evolutionary optimization algorithms Inventory management Machine learning Path planning Warehouse 4.0

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Analyzing evolutionary optimization and community detection algorithms using regression line dominance

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INFORMATION SCIENCES 2017年 396卷 185-201页

作者： Biswas, Anupam Biswas, Bhaskar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

In this paper, a visual analysis methodology is proposed to perform comparative analysis of guided random algorithms such as evolutionary optimization algorithms and community detection algorithms. Proposed methodology is designed based on quantile-quantile plot and regression analysis to compare performance of one algorithm over other algorithms. The methodology is extrapolated as one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, the many-to-many comparison i.e. ranking of algorithins is done only with solution quality. On the contrary, with proposed methodology ranking of algorithms is done in terms of both solution quality and convergence rate. Proposed methodology is studied with four evolutionary optimization algorithms on 25 benchmark functions. A non parametric statistical analysis called Wilcoxon signed-rank test is also performed to verify the indication of proposed methodology. Moreover, methodology is also applied to analyze four state-of-the-art community detection algorithms on 10 real-world networks. (C) 2017 Elsevier Inc. All rights reserved.

关键词： evolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis Community detection algorithms

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evolutionary based Pareto optimization algorithms for bi-objective PV array reconfiguration under partial shading conditions

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ENERGY CONVERSION AND MANAGEMENT 2022年 271卷

作者： Zhang, Xiaoshun Meng, Die Li, Wenji Yu, Tao Fan, Zhun Hao, Zhifeng Northeastern Univ Foshan Grad Sch Innovat Foshan 528311 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shantou Univ Coll Engn Shantou 515063 Peoples R China South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Peoples R China

During the daily operation of photovoltaic array, it easily faces the partial shading conditions resulted from the cloud shadow, dropping dust, etc. It will directly cause a lifetime reduction and a generation efficiency decre-ment for the photovoltaic array. To weaken the negative influence of partial shading condition, one of the most favoured ways is the photovoltaic array reconfiguration. However, the conventional photovoltaic array recon-figuration only aims to maximize the power output, which did not consider the lifetime and control complexity of switching devices. To fill up this gap, this paper constructs a new bi-objective optimization of photovoltaic array reconfiguration, which attempts to simultaneously maximize the output power and minimize the switching number. Consequently, it can dramatically reduce the switching control complexity while improving the gen-eration efficiency, while the operation life of the switching devices can be lengthened. In order to find a high -quality Pareto optimal reconfiguration schemes, six frequently-used evolutionary multi-objective optimization algorithms are employed to solve this bi-objective optimization. The effectiveness of bi-objective optimization of photovoltaic array reconfiguration is tested on three scales of total-cross-tied photovoltaic arrays under four partial shading patterns. The simulation results show that the maximum power increment by the proposed technique is up to 26.6% against to that without optimization, while the average switch number decrement is up to 31.1% compared with the single-objective optimization algorithms.

关键词： PV array reconfiguration Bi-objective optimization evolutionary optimization algorithms Partial shading condition

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evolutionary optimization for Active Debris Removal Mission Planning

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IEEE ACCESS 2023年 11卷 41019-41033页

作者： Zona, Danilo Zavoli, Alessandro Federici, Lorenzo Avanzini, Giulio Univ Salento Dept Engn Innovat I-73100 Lecce Italy Sapienza Univ Rome Dept Mech & Aerosp Engn I-00184 Rome Italy Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. A permutation-based encoding is introduced, which may handle multiple spacecraft trajectories. An original archipelago structure is also adopted for improving algorithm capabilities to explore the search space. As a further contribution, several crossover and mutation operators and migration schemes are tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighborhood of Sun-synchronous orbits, including cases with multiple chasers.

关键词： Costs Orbits Genetic algorithms optimization Space vehicles Planning Space debris Active debris removal evolutionary optimization algorithms space mission design

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Practical Application of Fractional-Order PID Controller based on evolutionary optimization Approach for a Magnetic Levitation System

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IETE JOURNAL OF RESEARCH 2023年第11期69卷 8168-8192页

作者： Dey, Soham Banerjee, Subrata Dey, Jayati Dr BC Roy Engn Coll Dept Elect Engn Durgapur India Natl Inst Technol Dept Elect Engn Durgapur India

This article proposes a state-of-the-art design procedure of integer-order PID (IOPID) and fractional-order PID (FOPID) controller applied to a well-established and diversified engineering application of the Magnetic Levitation System (Maglev). Controller design and implementation for the Maglev system are quite complicated and difficult since the system dynamics exhibits non-linearity with a wide variation of operating points. Also, the system is highly unstable which rules out the possibility to accomplish conventional tuning techniques. Thus in this work, the controller tuning methodology is framed as a complex optimization problem by incorporating a new transient specification-based objective function. For designing and tuning of proposed controller parameters, modern meta-heuristic and evolutionary optimization algorithms are deployed;those are Bird Swarm Algorithm, Elephant Herding optimization, Equilibrium optimizer and Grey Wolf optimization. The software and hardware results demonstrate that FOPID controllers yield better time-domain and frequency-domain performance specifications and exhibit excellent reference tracking capability than IOPID controllers. The performance robustness of the proposed controllers is greatly enhanced subjected to a vast range of parametric uncertainties along with a significant minimization of the control effort.

关键词： Equilibrium optimizer evolutionary optimization algorithms FOPID Intelligent control system design MAGLEV system Real-time control Robustness analysis

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Development of integrated evolutionary optimization algorithm and its application to optimum design of ship structures

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2008年第7期22卷 1313-1322页

作者： Kong, Young-Mo Choi, Su-Hyun Yang, Bo-Suk Choi, Byeong-Keun Pukyong Natl Univ Sch Mech Engn Pusan 608793 South Korea Daewoo Shipbldg & Marine Engn Ltd Geoje 656714 Gyungnam South Korea Gyeongsang Natl Univ Inst Marine Ind Sch Mech & Aerosp Engn Tongyoung 650160 Gyeongnam South Korea

This paper proposes an integrated evolutionary optimization algorithm (IEOA) which is combined with genetic algorithm (GA), random tabu search method (TS) and response surface methodology (RSM). This algorithm, in order to improve the convergent speed that is thought to be the demerit of GA, uses RSM and the simplex method. Though mutation of GA offers random variety, systematic variety can be secured through the use of tabu-list. Efficiency of this method has been proven by applying traditional test functions and comparing the results to GA. And it is an evidence that the newly suggested algorithm can effectively find the global optimum solution by applying it to minimize the weight of fresh water tank that is placed in the rear of ship designed to avoid resonance. According to the results, GA's convergent speed in initial phase has been improved by using RSM. An optimized solution was calculated without the evaluation of additional actual objective function. Finally, it can be concluded that IE0A is a very useful global optimization algorithm from the viewpoint of convergent speed and global search ability.

关键词： evolutionary optimization algorithms genetic algorithm response surface methodology tabu search method simplex method fresh water tank

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第3期236卷 1550-1570页

作者： Sardashti, A. Daniali, H. M. Varedi-Koulaei, S. M. Babol Univ Technol Dept Mech Engn Babol Iran Shahrood Univ Technol Dept Mech & Mechatron Engn POB 36199-95161 Shahrood Iran

This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similarity Error Function (GSEF), is introduced. Indeed, GSEF assesses the similarity between generated and desired paths, and its number of design variables is less than those in the other synthesis methods. Then, using an Innovative Adaptive Algorithm (IAA), some operators are utilized for matching two similar paths. GSEF-IAA methodology has some significant advantages over the reported synthesis methods. The method is fast, takes much less CPU time, and saves a large amount of computer memory. Four path generation problems are solved using GSEF-IAA, and the results are compared with those of previous methods using some well-known optimization algorithms to demonstrate the efficiency of GSEF-IAA methodology.

关键词： Four-bar mechanism path generation synthesis Geometrical Similarity Error Function Innovative Adaptive Algorithm evolutionary optimization algorithms

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Shuffled Frog Leaping Algorithm for Multi-objective Design optimization of Transverse Flux Linear Motor

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第11期44卷 1307-1315页

作者： Ali, Syed Q. Hasanien, Hany M. McGill Univ Dept Elect & Comp Engn Room 004McConnell Engn Bldg3480 Univ St Montreal PQ H3A 0E9 Canada Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt

This article presents a multi-objective design optimization of the transverse flux linear motor with an inner mover using the shuffled frog leaping algorithm. The optimization target is to reduce the transverse flux linear motor weight while maximizing its thrust force as well as minimizing its detent force. The design variables of the optimization problem are the stator pole length, air-gap length, winding window width, and stator pole width, where these variables affect the motor characteristics. The response surface methodology was used to obtain a mathematical model of the motor weight, detent force, and thrust force in terms of the design variables. Finite-element computations were used for numerical experiments on the geometrical design variables to determine the coefficients of a second-order analytical model for the response surface methodology. The finite-element analysis model is verified with the experimental results of a scaled-down model of the transverse flux linear motor. The effectiveness of the shuffled frog leaping algorithm model is compared with that of the genetic algorithm model and particle swarm optimization model. With the proposed shuffled frog leaping algorithm technique, the weight of the initially designed transverse flux linear motor and its detent force is reduced and its thrust force is increased.

关键词： transverse flux linear motor design optimization shuffled frog leaping algorithm finite-element method response surface methodology thrust force optimization permanent magnet motors linear motors evolutionary optimization algorithms three-dimensional finite-element analysis

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