R-method: A simple ranking method for multi-attribute decision-making in the industrial environment

A simple multi-attribute decision-making method based on ranking of alternatives and attributes is proposed in this paper. The method ranks the alternatives with respect to each of the attributes based on the corresponding performance measures. Similarly, the ranks are assigned to the attributes based on their importance as perceived by the decision maker. The ranks assigned to the alternatives with respect to each of the attributes and the ranks assigned to the attributes are converted to appropriate weights and the final composite scores of the alternatives are computed using these weights. An interesting feature of the proposed method is that the qualitative attributes (i.e. the attributes expressed in linguistic terms) need not require the use of fuzzy logic. The proposed method is very simple and useful in situations of limited time availability, presence of qualitative attributes, imprecise/incomplete/partial data, and decision maker’s limited attention and capability to process the information. The proposed method is proved easier and better compared to the other widely used decision-making methods. The proposed method will be tested further on more realistic problems of the industrial environment and the results will be reported soon

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification

Multiobjective optimization of underground power cable systems

This paper presents a modified Jaya algorithm (MJaya) for optimizing the material costs and electricthermal performance of an Underground Power Cable System (UPCS). Three power cables arranged in flat formation are considered. Three XLPE high voltage cables are situated in the thermal backfill layer for ensuring the optimal thermal performance of the cable system. The cable backfill dimensions, cable backfill material, and cable conductor area are selected as design variables in the optimization problem. In the study, the Finite Element Method model is validated experimentally. The Particle Swarm Optimization (PSO), Jaya, and MJaya algorithms are used for multiobjective optimization in order to design a cable system in such a way to minimize the cable backfill costs and maximize the allowable electric current flowing through the cables. For the case study, calculations performed using the Jaya algorithm indicated 1.7 mln Euro cable system costs while cable ampacity is equal to I ¼ 1460 A. The calculations are performed for the objective function values equal to w1 ¼ 0.5 and w2 ¼ 0.5. Such an optimization parameters set allow obtaining low costs of UPCS alongside with reasonable cable line ampacity. What is more, the results of the optimization obtained by Jaya, MJaya, and PSO algorithms are compared. Therefore, Coverage and Hypervolume metrics are incorporated. It is concluded that both the Jaya and MJaya algorithms performed better when compared to the PSO algorithm

Multipopulation-based multi-level parallel enhanced Jaya algorithms

To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R and Grant TIN2017-89266-R, co-financed by FEDER funds (MINECO/FEDER/UE)

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, in part by FEDER funds (MINECO/FEDER/UE), and in part by the Spanish Ministry of Science, Innovation, and Universities co-financed by FEDER funds under Grant RTI2018-098156-B-C54

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grants TIN2015-66972-C5-4-R and TIN2017-89266-R, co-financed by FEDER funds. (MINECO/FEDER/UE)

PLANT LOCATION SELECTION USING FUZZY DIGRAPH AND MATRIX METHODS

In the present work, a methodology based on fuzzy digraph and matrix methods is developed for evaluation of alternative plant locations. Attributes which characterize plant location selection are identified and are called the 'plant location attributes'. Consideration of these attributes and their interrelations are rudiment in evaluation. Tbis is modeled in terms of a 'plant location attributes digraph'. The digraph is represented by a one-to-one matrix and the 'permanent function' of this matrix leads to the development of a characteristic expression, which is useful in comparing the alternative plant locations. 'Plant location selection index' is obtained from the 'pennanent function' of the matrix by substituting numerical values of the attributes and their interrelations. A step by step procedure for evaluation of 'plant location selection index' is suggested. The methodology is illustrated by means of an example

Parallel implementation of metaheuristics for optimizing tool path computation on CNC machining

The incorporation of technological advances in industry is a must, even for traditional sectors where most companies are SMEs and investments are limited. Technology can be used to increase productivity and the quality of the manufactured product. Drilling is a common procedure in industry. It usually consists of multiple drilling of a flat surface with a tool. Usually the tool is placed on the surface to be drilled at a safe distance and then it makes the drilling in a linear fashion. Optimization of the tool path often involves reducing the movement of the tool to place it over the next point to be drilled, known as airtime. The problem of minimizing airtime for drill paths is highly complex. Most proposals to solve the problem try to adapt it to the formulation of the Traveling Salesman Problem (TSP), in which the objective is to navigate a list of nodes using the minimum global distance. In this paper, the purpose is to provide a solution to the TSP applied to tool path optimization by means of a Discrete version of the Teacher-Learner-Based Optimization (TLBO) algorithm. To improve performance, the algorithm is implemented using a parallel Computer Unified Device Architecture (CUDA) and run on a manycore Graphical Processing Unit (GPU). The results show that the parallel implementation of Discrete TLBO is faster than 9x the sequential implementation.This research was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under the project CloudDriver4Industry TIN2017-89266-R, and by the Spanish Ministry of Science, Innovation and Universities, the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under Grant RTI2018-098156-B-C54

Inverse problem for dynamic structural health monitoring based on slime mould algorithm

In this paper, damage detection, localization and quantification are performed using modal strain energy change ratio (MSEcr) as damage indicator combined with a new optimization technique, namely slime mould algorithm (SMA) developed in 2020. The SMA algorithm is employed to assess structural damage and monitor structural health. Two structures, including a laboratory beam and a bar planar truss are considered to study the effectiveness of the proposed approach. Another recent algorithm called marine predators algorithm (MPA) is also used for comparison purposes with SMA. The MSEcr is utilized in the first stage to predict the location of the damaged elements. Single and multiple damages cases are analysed based on different number of modes to study the sensitivity of the proposed indicator to the total number of modes considered in the analysis. Next, this indicator is used as an objective function in a second stage to solve the inverse problem using SMA and MPA for damage quantification of the elements identified in the first stage. Experimental validation is conducted using a 3D frame structure with four stories that have damaged components. It is demonstrated that the proposed approach, using MSEcr and SMA, provides superior results for the considered structures. The effectiveness of this technique is tested by introducing a white Gaussian noise with different levels, namely 2% and 4%. The results show that the provided approach can predict the location and level of damage with high accuracy after introducing the noise

A new metaphor-less simple algorithm based on Rao algorithms: a Fully Informed Search Algorithm (FISA)

Many important engineering optimization problems require a strong and simple optimization algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric algorithms, known as Rao algorithms, which have garnered significant attention from researchers worldwide due to their simplicity and effectiveness in solving optimization problems. In our simulation studies, we have developed a new version of the Rao algorithm called the Fully Informed Search Algorithm (FISA), which demonstrates acceptable performance in optimizing real-world problems while maintaining the simplicity and non-parametric nature of the original algorithms. We evaluate the effectiveness of the suggested FISA approach by applying it to optimize the shifted benchmark functions, such as those provided in CEC 2005 and CEC 2014, and by using it to design mechanical system components. We compare the results of FISA to those obtained using the original RAO method. The outcomes obtained indicate the efficacy of the proposed new algorithm, FISA, in achieving optimized solutions for the aforementioned problems. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/FISA