This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF)...
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This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.
Global optimisation method Differential migration (DM) with restarting is described in this paper and evaluated together with Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IP...
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ISBN:
(纸本)9781509064359
Global optimisation method Differential migration (DM) with restarting is described in this paper and evaluated together with Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IPOP-CMA-ES). Differential migration is another step in global optimisation from SOMA (self-organizing migration algorithm) combining two basic individual movement methods of SOMA - all-to-one and all-to-all, via cluster analysis and internal algorithm constant defining continuous change from one type of movement to another. The proposed algorithm implements essential ideas of Differential Evolution regardless of their original interpretation in living nature with subsequent increase of efficiency in finding global extreme which holds mainly for noisy multimodal cost functions present in the benchmarks as well as in real world applications.
Tolerancing is an important issue in product and manufacturing process designs. The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances in the intermediate mach...
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Tolerancing is an important issue in product and manufacturing process designs. The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances in the intermediate machining steps of component fabrication can significantly affect the quality, robustness and life-cycle of a product. Stimulated by the growing demand for improving the reliability and performance of manufacturing process designs, the tolerance design optimization has been receiving significant attention from researchers in the field. In recent years, it broad class of meta-heuristics algorithms has been developed for tolerance optimization. Recently, a new class of stochastic optimization algorithm called self-organizing migrating algorithm (SOMA) was proposed in literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a "social environment". This paper introduces it modified SOMA approach based on Gaussian operator (GSOMA) to solve the machining tolerance allocation of an overrunning clutch assembly. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. Simulation results obtained by the SOMA and GSOMA approaches are compared with results presented in recent literature using geometric programming, genetic algorithm, and particle swarm optimization. (C) 2009 IMACS. Published by Elsevier B.V. All rights reserved.
This article is aimed to using the analytical programming and the Use Case Points method to estimate time effort in software engineering. The calculation of Use Case Points method is strictly algorithmically defined, ...
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ISBN:
(纸本)9783319067407
This article is aimed to using the analytical programming and the Use Case Points method to estimate time effort in software engineering. The calculation of Use Case Points method is strictly algorithmically defined, and the calculation of this method is simple and fast. Despite a lot of research on this field, there are many attempts to calibrating the weights of Use Case Points method. In this paper is described idea that equation used in Use Case Points method could be less accurate in estimation than other equations. The aim of this research is to create new method, that will be able to create new equations for Use Case Points method. Analytical programming with self-organizing migration algorithm is used for this task. The experimental results shows that this method improving accuracy of effort estimation by 25-40 %.
In this article, the performance of a self-organizing migration algorithm (SOMA), a new stochastic optimization algorithm, has been compared with a genetic algorithm with floating-point representation (GAF) and differ...
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In this article, the performance of a self-organizing migration algorithm (SOMA), a new stochastic optimization algorithm, has been compared with a genetic algorithm with floating-point representation (GAF) and differential evolution (DE) for an engineering application. This application is the estimation of the apparent thermal conductivity of foods at freezing temperature using an inverse method. Assuming two piecewise functions for apparent thermal conductivity in function of the temperature data, the heat diffusion equation was solved to estimate the unknown variables of inverse problem. The thermal conductivity is continuously adjusted by three approaches of stochastic optimization algorithms, used to minimize a performance criterion based on error information for the inverse problem. The variables that provide the best fitness between the experimental and predicted time-temperature curves at centre of the food under freezing conditions were obtained. Moreover, a statistical analysis showed the agreement between reported and estimated curves. In this application domain, the SOMA and DE approaches outperform the GAF.
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