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1

The efficiency of detecting the failures and troubleshooting while applying technical diagnostics for multi-computer systems

Ye K. Z., Kyaw H. A., Portnov E. M., Bain A. M., Vasant P.

Archives of Control Sciences

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2015

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Vol. 25, no. 1

87--107

EN

This paper presents techniques which base on the concept of flows thinning together with the identification techniques. These methods are proposed to determine the expected number of failures to assess the efficiency of technical diagnostics of instruments. Additionally, this research focuses on the improvement of multi-machine troubleshooting systems, based on the ‘AND-OR’ graphs. Respective algorithms are presented. The majority principle uses the input information to check the correctness of the decision regarding identification of faulty machines. In this paper we base on the complete testing algorithm for elements of multi-computer complexes searching by criteria of failed element.

2

Optimization of thermo-electric coolers using hybrid genetic algorithm and simulated annealing

Khanh D. V. K., Vasant P., Elamvazuthi I., Dieu V. N.

Archives of Control Sciences

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2014

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Vol. 24, no. 2

155--176

EN

Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to their superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environmentally friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single-objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) and Simulated Annealing (SA) to optimize geometry properties so that TECs will operate at near optimal conditions. Equality constraint and inequality constraint were taken into consideration.

3

A hybrid PSO approach for solving non-convex optimization problems

Ganesan T., Vasant P., Elamvazuthy I.

Archives of Control Sciences

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2012

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Vol. 22, no. 1

87-105

EN

The aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.

4

Hybrid Mesh Adaptive Direct Search and Genetic Algorithms Techniques for industrial production systems

Vasant P.

Archives of Control Sciences

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2011

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Vol. 21, no. 3

299-312

EN

In this paper, computational and simulation results are presented for the performance of the fitness function, decision variables and CPU time of the proposed hybridization method of Mesh Adaptive Direct Search (MADS) and Genetic Algorithm (GA). MADS is a class of direct search of algorithms for nonlinear optimization. The MADS algorithm is a modification of the Pattern Search (PS) algorithm. The algorithms differ in how the set of points forming the mesh is computed. The PS algorithm uses fixed direction vectors, whereas the MADS algorithm uses random selection of vectors to define the mesh. A key advantage of MADS over PS is that local exploration of the space of variables is not restricted to a finite number of directions (poll directions). This is the primary drawback of PS algorithms, and therefore the main motivation in using MADS to solve the industrial production planning problem is to overcome this restriction. A thorough investigation on hybrid MADS and GA is performed for the quality of the best fitness function, decision variables and computational CPU time.

5

The Optimization of a Revenue Function in a Fuzzy Linear Programming Model Used for Industrial Production Planning

Vasant P., Barsoum N, Webb J F

Journal of Applied Computer Science Methods

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2009

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Vol. 1 No. 2

65--83

EN

In this paper, the S-curve membership function methodology is used in a reallife industrial problem in which there are various products, each of which requires a certain mix of raw materials selected from a set of available raw materials. This problem occurs in the chocolate manufacturing industry where decision makers and implementers play important roles that enable successful manufacturing of the products in an uncertain environment. The analysis in this paper tries to find a solution that helps a decision maker when deciding on what to implement. This problem is considered because it can be modeled with the help of fuzzy parameters (for example, the availability of raw materials is not always certain, and so can be treated as a fuzzy parameter). With 29 constraints and 8 variables the problem here is sufficiently large for the S-curve methodology employed because this methodology is applicable to problems with as few as 1 constraint and 1 variable. A decision maker can specify which vagueness parameter  is suitable for achieving a revenue which through the analysis results in an initial solution that can be implemented. From the results of this implementation the decision maker can then suggest some possible and practicable changes in fuzzy intervals for improving the revenue. Within the framework of the analysis this interactive process has to go on between the decision maker and the implementer until an optimum solution is achieved and implemented.