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nature inspired algorithms for the solution of inverse heat transfer problems applied to distinct unsteady heat flux orientations in cylindrical castings

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第5期34卷 2407-2430页

作者： da Silva-Santos, Carlos Henrique Goulart, Pedro Roberto Bertelli, Felipe Garcia, Amauri Cheung, Noe Fed Inst Educ Sci & Technol Sao Paulo IFSP Itapetininga Brazil Univ Fed Sao Paulo Marine Sci Inst UNIFESP BR-11070100 Santos SP Brazil Univ Estadual Campinas UNICAMP Dept Mfg & Mat Engn Campinas Brazil

One of the most important parameters in the foundry design of castings is the heat transfer coefficient at the casting/mold surface (h), which significantly affects the solidification cooling rate and defines the as-cast microstructure and, consequently, its properties. The inverse heat transfer problem (IHTP) is a needed task to be solved in many casting processes to evaluate this coefficient. In this work, four different nature inspired algorithms [genetic algorithm (GA), evolutionary strategy (ES), artificial immune system (AIS) and particle swarm algorithm (PSO)] have been integrated to a numerical model of solidification to solve the IHTP problem with a view to evaluating the transient profile of h during solidification of three different cylindrical casting setups (outward, inward and upward). The aim is to reduce the number of iterations to achieve successful results by the association between metaheuristics and different sets of h values. Therefore, an objective function was modeled to match the temperatures predicted by the numerical model to those of experimental measurements. Sets of 10, 20, 50, 100 and 200 h values used to compose its transient profile have been evaluated by the four metaheuristics approaches aiming to characterize each casting situation.

关键词： Solidification Inverse heat transfer problem (IHTP) Numerical modeling nature inspired algorithms Metaheuristic optimization

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On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters

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APPLIED SOFT COMPUTING 2016年 46卷 665-676页

作者： Cruz-Aceves, Ivan Hernandez-Aguirre, Arturo Ivvan Valdez, S. Ctr Invest Matemat CIMAT AC Jalisco S-N Guanajuato 36000 Gto Mexico

This paper presents a comparative analysis of four nature inspired algorithms to improve the training stage of a segmentation strategy based on Gaussian matched filters (GMF) for X-ray coronary angiograms. The statistical results reveal that the method of differential evolution (DE) outperforms the considered algorithms in terms of convergence to the optimal solution. From the potential solutions acquired by DE, the area (At) under the receiver operating characteristic curve is used as fitness function to establish the best GMF parameters. The GMF-DE method demonstrated high accuracy with A(z) = 0.9402 with a training set of 40 angiograms. Moreover, to evaluate the performance of the coronary artery segmentation method compared to the ground-truth vessels hand-labeled by a specialist, measures of sensitivity, specificity and accuracy have been adopted. According to the experimental results, GMF-DE has obtained high coronary artery segmentation rate compared with six state-of-the-art methods provided an average accuracy of 0.9134 with a test set of 40 angiograms. Additionally, the experimental results in terms of segmentation accuracy, have also shown that the GMF-DE can be highly suitable for clinical decision support in cardiology. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Automatic segmentation Coronary angiograms Gaussian matched filters Medical imaging nature inspired algorithms Vessel detection

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Emergent nature inspired algorithms for multi-objective optimization

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COMPUTERS & OPERATIONS RESEARCH 2013年第6期40卷 1521-1523页

作者： Figueira, Jose Rui Talbi, El-Ghazali Univ Tecn Lisboa Inst Super Tecn Lisbon Portugal Univ Lille CNRS INRIA Lille France

Many real-world decision-making situations possess both a discrete and combinatorial structure and involve the simultaneous consideration of conflicting objectives. Problems of this kind are in general of large size and contains several objectives to be "optimized". Although Multiple Objective Optimization is a well-established field of research, one branch, namely nature inspired metaheuristics is currently experienced a tremendous growth. Over the last few years, developments of new methodologies, methods, and techniques to deal with multi-objective large size problems in particular those with a combinatorial structure and the strong improvement on computing technologies (during and after the 80s) made possible to solve very hard problems with the help of inspired nature based metaheuristics. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Metaheuristics Multi-objective optimization nature inspired algorithms

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A Survey on Parallel nature inspired algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1893-1918页

作者： Kumar, Lalit Pandey, Manish Ahirwal, Mitul Kumar Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462003 MP India

Many computation tasks in science and engineering with large amount of data involve complex optimization problems for researchers. nature inspired based algorithms (NIAs) are mostly know for solving complex problems in real world but also these algorithms have one of the major drawbacks of computation cost due to complex problems or increase in the dimension of the problems. Hence, several efforts are made to achieve better computation cost by using parallelization in nature inspired algorithms (NIAs). Parallel nature inspired algorithms (P-NIAs) come forward to overcome the cost of these type of optimization problems. Naturally some of the NIAs are inherently parallel and known for fast processing task. This survey presents a systematic review of existing literature related to parallelization in NIAs. We offered a systematic search to identify the literature through well-reputed publications as IEEE, Elsevier, Springer, ACM, Hindawi, Inderscience, and Wiley. Out of 100 studies 77 reputable studies were selected to identify the current research trends, challenges and opportunities for parallelization in NIAs. Moreover, some problems are also discussed related to algorithm's computation and performance.

关键词： nature inspired algorithms Optimization problems Parallel computing Systematic search Parallelization Algorithm's computation

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Advanced design optimization on straight bevel gears pair based on nature inspired algorithms

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SN APPLIED SCIENCES 2019年第10期1卷 1155页

作者： Ebenezer, N. Godwin Raja Ramabalan, S. Navaneethasanthakumar, S. Loyola ICAM Coll Engn & Technol Dept Mech Engn Chennai 600034 Tamil Nadu India EGS Pillay Engn Coll Dept Mech Engn Nagapattinam 611002 Tamil Nadu India MLR Inst Technol Dept Mech Engn Hyderabad 500043 Telangana India

Gears are used to transmit mechanical power in systems such as automotives, automation and machine tools. The demand for lighter and optimally designed gears is high in power transmission systems, as they save material, energy and also considerably influence performance. Hence, in this paper, a bevel gear pair is optimized. The problem consists of a non linear objective function, four design variables and eight inequality constraints. The objective is to minimize the volume of the gear. The design variables are: number of teeth, module, face width and diameter of the shaft, which is a new addition. Apart from considering regular mechanical constraints, six other additional critical constraints on contact ratio, load carrying capacity, power loss, root not cut, no involute interference and line of action are also included. nature inspired algorithms, namely, simulated annealing, fire fly, cuckoo search and fmincon solver are employed in MATLAB environment. Results of simulation are analysed, compared and validated with literature.

关键词： Design optimization Bevel gear AGMA nature inspired algorithms

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Swarm intelligence and nature inspired algorithms for solving vehicle routing problems: a survey

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OPERATIONAL RESEARCH 2024年第3期24卷 47页

作者： Stamadianos, Themistoklis Taxidou, Andromachi Marinaki, Magdalene Marinakis, Yannis Tech Univ Crete Sch Prod Engn & Management Univ Campus Akrotiri 73100 Chania Greece

Vehicle routing problem (VRP) is a classic NP-hard optimization problem. It is generally accepted that an optimized routing scheme can cause huge difference in the cost in all stages of transportation. Consequently, the VRP has evoked interest among the researchers of the field. Usually, a metaheuristic or an evolutionary algorithm is used for the solution of a VRP variant. In the last years, a number of swarm intelligence algorithms have been used for the solution of the problem. Initially, the two most classic swarm intelligence algorithms, the Ant Colony Optimization and the Particle Swarm Optimization, were used for the solution of this kind of problems. However, in the last years, more and more researchers solved the problem using a different swarm intelligence algorithm. In this paper, we focused in the presentation and analysis of the swarm intelligence algorithms that have been used for the solution of the problem. We give the advantages and disadvantages of each method, we focus in those ones that produced the best results in difficult VRPs and we present directions for the future of this kind of algorithms for the solution of a VRP variant.

关键词： Vehicle routing problem Swarm intelligence nature inspired algorithms Particle Swarm Optimization Ant Colony Optimization Firefly Algorithm Bat Algorithm Honey Bees Mating Optimization Algorithm Bumble Bees Mating Optimization Algorithm Cuckoo Search

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Design optimisation of mating helical gears with profile shift using nature inspired algorithms

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AUSTRALIAN JOURNAL OF MECHANICAL ENGINEERING 2022年第4期20卷 903-910页

作者： Ebenezer, N. Godwin Raja Ramabalan, S. Navaneethasanthakumar, S. Loyola ICAM Coll Engn & Technol Dept Mech Engn Chennai Tamil Nadu India EGS Pillay Engn Coll Dept Mech Engn Nagapattinam India Sri Subramanya Coll Engn & Technol Dept Mech Engn Palani India

Pro?le shift of the gear significantly affects its form, zone, stress factors, transversal and face load factors. In this paper, an optimum solution to the problem of mating helical gears design considering profile shift is given. The objective of the problem is to minimise volume taking into account tooth root bending strength, contact pressure, face width, and addendum modification coefficients as constraints. The design variables, namely, the module, the face width, the number of teeth, and pro?le shift coefficients of both gears are considered. nature-inspired algorithms, namely, Simulated Algorithm (SA), Fire Fly Algorithm (FA), Cuckoo Search (CS), Particle Swarm Optimisation (PSO) and Teaching Learning-Based Optimisation (TLBO) algorithms are employed. Results of simulation with and without profile shift, performance of various algorithms are analysed and validated with literature. Results show that CS and PSO algorithms give the best optimum volume. They also significantly reduce volume by 1.91%, when compared to literature. CS algorithm gives the best optimum design parameters. Furthermore, results show the volume of the mating helical gears with profile shift is much lesser than the pair without profile shift.

关键词： Design OPTIMISATION helical gear profile shift nature inspired algorithms TLBO

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Clock Drift Management Using nature inspired algorithms

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2012年第4期5卷 48-62页

作者： Tekchandani, Prakash Trivedi, Aditya Tekchandani, Rajkumar ABV Indian Inst Informat Technol & Management Gwalior Madhya Pradesh India Thapar Univ Patiala Patiala India

Time Synchronization is common requirement for most network applications. It is particularly essential in a Wireless Sensor Networks (WSNs) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, we optimized FTSP for clock drift management using Particle Swarm Optimization (PSO), Variant of PSO and Differential Evolution (DE). The paper estimates the clock offset, clock skew, generates linear line and optimizes the value of average time synchronization error using PSO, Variant of PSO and DE. In this paper we present implementation and experimental results that produces reduced average time synchronization error using PSO, Variant of PSO and DE, compared to that of linear regression used in FTSP.

关键词： Average Time Synchronization Error Clock Drift Computer Science nature inspired algorithms Time Synchronization Wireless Sensor Network

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A Framework to Mine Communities Using nature inspired algorithms 2

A Framework to Mine Communities Using Nature Inspired Algori...

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2nd IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Arora, Nidhi Banati, Hema Univ Delhi Kalindi Coll Dept Comp Sci Delhi India Univ Delhi Dyal Singh Coll Dept Comp Sci Delhi India

ISBN: (纸本)9781509010479

Natural intelligence heuristic techniques have demonstrated their capability to provide acceptable solutions to many real life complex problems. Their potential to mine communities from complex networks has been successfully tested by many researchers. With the growing rate of development of new robust and efficient nature based algorithms, a strong need is felt for a generalized framework to evolve communities which can accommodate any existing or new nature based algorithm. This paper proposes a framework for applying natural intelligence based optimization strategies to detect communities using any nature inspired algorithm. The proposed framework can serve as an abstraction for evaluating a set of new/existing nature based methodologies for detecting communities at varied level of optimalities in order to select the most efficient algorithm. Extraction of communities at varied optimality levels by considered algorithms can reveal multiple grouping patterns prevailing in the complex network and can help in planning strategic decision. The framework consists of four prominent, independent phases: The Analysis, Initialization, Evolution and Result Generation Phase. Evaluation of any new/existing metaheuristic or evolutionary algorithm for community detection may be simply done by modifying the Evolution Phase. The framework thus provides an easy to use flexible platform for use by researchers in the domain of community detection.

关键词： community nature inspired algorithms heuristic optimization framework

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Application of nature inspired algorithms in Power Delivery Network Design : An Industrial Case Study 23

Application of Nature Inspired Algorithms in Power Delivery ...

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23rd IEEE Conference on Electrical Performance of Electronic Packaging and Systems

作者： Tripathi, Jai Narayan Mukherjee, Jayanta Nagpal, Raj Kumar Malik, Rakesh STMicroelect Pvt Ltd TR&D Greater Noida India Indian Inst Technol Dept Elect Engn Bombay Maharashtra India

ISBN: (纸本)9781479936434

This paper presents application of nature inspired algorithms for solving an industrial problem of power delivery network design. To achieve the target impedance in a power delivery network, optimum number of decoupling capacitors and their optimum locations on board are found from thousands of available capacitors. S-parameters based data is used for the accurate analysis. Three natural computing algorithms - Particle Swarm Optimization, Cuckoo Search and Firefly Algorithm are used and compared for solving this problem.

关键词： Power Integrity Power Delivery Networks Decoupling Capacitors S-parameters nature inspired algorithms Swarm Intelligence

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