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In this paper a global optimization algorithm is proposed for solving minimax linear fractional programming problem (P). By utilizing equivalent problem ( Q ) and linearization technique, the relaxation linear programming (RLP) about the (Q) is established. The proposed branch and bound algorithm is convergent to the global minimum of (Q) through the successive refinement linear relaxation of the...
In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed-point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on an J1 subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and mutation operators relying on the integer labels are designed. In this case, whether every individual...
Truncation method has been proven to be effective for computing singular minimizers or singular minimizing sequences in variational problems involving the Lavrentiev phenomenon. But truncation area involving the singular set must be selected first when using the truncation method. In this paper three numerical methods for selecting truncation area are introduced, consequently which can detect singular...
The coexistence of multimedia services in e-communication systems, with varying bandwidth utilization characteristics, impedes the efficiency of rate control and thereby impacts on the Quality of Service (QoS), in terms of low throughput. As such, the rate control for multimedia flows remains an open problem. This paper proposes a memetic optimization approach to rate allocation of multiclass services...
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...
PROMETHE II is a multicriteria decision aid method based on pairwise comparisons. As a consequence, it cannot reasonably be applied to problems involving a large number of alternatives (such as for instance in combinatorial or continuous multi-objective optimization). The aim of this paper is to present an approach inspired by genetic algorithms that allows to overcome these difficulties. An illustrative...
Many real world problems which can be assigned to the machine learning domain are inverse problems. The available data is often noisy and may contain outliers, which requires the application of global optimization. Evolutionary Algorithms (EA's) are one class of possible global optimization methods for solving such problems. Within population based EA's, Differential Evolution (DE) is a widely used...
We investigate stochastic averaging on infinite time interval for a class of continuous-time nonlinear systems with stochastic perturbation and remove several restrictions present in existing results: global Lipschitzness of the nonlinear vector field, equilibrium preservation under the stochastic perturbation, and compactness of the state space of the perturbation process. If an equilibrium of the...
Generalized Nash equilibria (GNE) represent extensions of the Nash solution concept when agents have shared strategy sets. This generalization is particularly relevant when agents compete in a networked setting. In this paper, we consider such a setting and focus on a congestion game in which agents contend with shared network constraints. We make two sets of contributions: (1) Under two types of...
Necessary and sufficient stability conditions are given for the existence of a continuous Lyapunov function for a semicontinuous, stochastic discrete-time system. The continuity of the Lyapunov function is linked to robustness of the stability property, which reduces to classical stability plus convergence for deterministic systems. The nature of the Lyapunov results are inspired by Lyapunov results...
We consider control systems of the type x?? = Ax+??(t)ub, where u ?? R, (A; b) is a controllable pair and ?? is an unknown time-varying signal with values in [0; 1] satisfying a permanent excitation condition of the kind ??t+Tt ?? ?? ??for 0 < ?? ?? T independent on t. We prove that such a system is stabilizable with a linear feedback depending only on the pair (T; ??) if the real part of the...
The present paper considers distributed consensus algorithms for agents evolving on a connected compact homogeneous (CCH) manifold. The agents track no external reference and communicate their relative state according to an interconnection graph. The paper first formalizes the consensus problem for synchronization (i.e. maximizing the consensus) and balancing (i.e. minimizing the consensus); it thereby...
A global finite-time observer is designed for nonlinear systems which are uniformly observable and globally Lipschitz. This result is based on a high-gain approach.
First, a quantized progressive second price (PSP) auction mechanism called the Unique Limit Quantized - PSP (UQ-PSP) is presented for the allocation of fixed or timevarying quantities of a resource among arbitrary populations of agents. It is shown that (i) the states (i.e. bid prices and quantities) of the corresponding iterative dynamical auction system converge to a unique quantized (Nash) equilibrium...
In this paper we give conditions that a discrete time switched linear systems must satisfy if it is stable. We do this by calculating the mean and covariance of the set of matrices obtained by using all possible switches. The theory of switched linear systems has received considerable attention in the systems theory literature in the last two decades. However, for discrete time switched systems the...
Based on the max-min ant system solving the Pseudo-Boolean Optimization problem, we improved the process of solving the problem. An memory array is introduced to record changes of each bit string, memory array to be used for optimization of the solution of the current iteration. Such improvements will help improve the convergence rate of Max-Min ant colony algorithm. The improved algorithm was applied...
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...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
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