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Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been suggested in literature out of which stochastic, population...
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant...
Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.), which modify the shape of the search space. We propose an ecologically inspired invasive weed optimization (IWO) algorithm to solve the constrained real-parameter optimization problems. Central to our approach is a parameter-free penalty function that we introduce. The adaptive nature of the penalty...
A model of the bee hive that clearly separates the self-organizing decision-making behaviour of the bees in the hive and the problem-specific behaviour of the bees outside the hive is presented. This separation allows for the applications of the model for different problem domains. Results of the application to three problem domains are presented - web search, function optimization and hierarchical...
This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high...
In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with...
Differential evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values...
Multiprocessor task scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant progress recently. However, it seems not to imply practical algorithms for the problem yet. In this paper, the offline version of the Multiprocessor scheduling problem on m-processor system is discussed for the case...
Sustainable development is widely practiced by many engineers, academicians and policy makers for making the industries environmental friendly, to obtain sustainability in any industry there are many methods. Resources utilized in an industry should be identified properly in various processes and its utilization is to be optimized to increase the environmental performance of the industry. Ecomapping...
The properties of value vector and coefficient vector of symmetric Boolean functions with maximum algebraic immunity are studied by applying combinatory and Lucas formula. Basing on that, we proved the algebraic immunity of three classes (2m -2)-variable symmetric Boolean functions with mges3 is not maximum.
This paper proposes an approach to customer value assessment based on Max-Entropy and TOPSIS, while customerspsila information are interval numbers. The decision matrix in the form of interval is normalized and transformed into a definite one based on maximizing the entropy of the decision matrix; The weights of attributes are then determined, which are used in assessing the alternatives based on...
In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application...
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