A new multi objective optimization approach in distribution systems

This paper presents a multi objective optimal location of AVRs in distribution systems at the presence of distributed generators based on modified teaching-learning-based optimization (MTLBO) algorithm. In the proposed MTLBO algorithm, teacher and learner phases are modified. The proposed objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm utilizes several teachers and considers the teachers as an external repository to save found Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as GA, PSO and TLBO, is extraordinary.     

On the simulation of textile reinforced concrete using a multi scale approach

Textile reinforced concrete (TRC) is a composite of textile structures made of multi-filament yarns (rovings) within a cementitious matrix. Experimental investigations of textile reinforced concrete specimen show very complex failure mechanisms on different length scales. Therefore mechanical models on the micro, meso and macro scale are introduced. The paper presents a hierarchical material model of TRC on three scales. While on the micro scale the individual filaments of the fiber bundles are distinguished to determine an effective roving behavior, models on the meso scale are used to predict the macroscopic response of the composite material. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A genetic algorithm approach to school timetabling

An adaptive algorithm based on computational intelligence techniques is designed, developed and applied to the timetabling problem of educational organizations. The proposed genetic algorithm is used in order to create feasible and efficient timetables for high schools in Greece. In order to demonstrate the efficiency of the proposed genetic algorithm, exhaustive experiments with real-world input data coming from many different high schools in the city of Patras have been conducted. As well as that, in order to demonstrate the superior performance of the proposed algorithm, we compare its experimental results with the results obtained by another effective algorithm applied to the same problem. Simulation results showed that the proposed algorithm outperforms other existing attempts. However, the most significant contribution of the paper is that the proposed algorithm allows for criteria adaptation, thus producing different timetables for different constraints priorities. So, the proposed approach, due to its inherent adaptive capabilities, can be used, each time satisfying different specific constraints, in order to lead to different timetables, thus meeting the different needs that each school may have.     

A new approach to rank one linear algebraic groups

One can develop the basic structure theory of linear algebraic groups (the root system, Bruhat decomposition, etc.) in a way that bypasses several major steps of the standard development, including the self-normalizing property of Borel subgroups.     

New fitness sharing approach for multi-objective genetic algorithms

A novel fitness sharing method for MOGA (Multi-Objective Genetic Algorithm) is proposed by combining a new sharing function and sided degradations in the sharing process, with preference to either of two close solutions. The modified MOGA adopting the new sharing approach is named as MOGAS. Three different variants of MOGAS are tested; MOGASc, MOGASp and MOGASd, favoring children over parents, parents over children and solutions closer to the ideal point, respectively. The variants of MOGAS are compared with MOGA and other state-of-the-art multi-objective evolutionary algorithms such as IBEA, HypE, NSGA-II and SPEA2. The new method shows significant performance improvements from MOGA and is very competitive against other Evolutionary Multi-objective Algorithms (EMOAs) for the ZDT and DTLZ test functions with two and three objectives. Among the three variants MOGASd is found to give the best results for the test problems.     

A genetic approach for strategic resource allocation planning

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource allocation.     

A probabilistic approach to one class of nonlinear differential equations

Summary We establish connections between positive solutions of one class of nonlinear partial differential equations and hitting probabilities and additive functionals of superdiffusion processes. As an application, we improve results on superprocesses by using the recent progress in the theory of removable singularities for differential equations.Partially supported by National Science Foundation Grant DMS-8802667     

Personal income tax reforms: A genetic algorithm approach

Given a settled reduction in the present level of tax revenue, and by exploring a very large combinatorial space of tax structures, in this paper we employ a genetic algorithm in order to determine the ‘best’ structure of a real world personal income tax that allows for the maximisation of the redistributive effect of the tax, while preventing all taxpayers being worse off than with the present tax structure. We take Italy as a case study.     

A genetic algorithm approach to nonlinear least squares estimation

A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than global optimum. Genetic algorithms have been applied successfully to function optimization and therefore would be effective for nonlinear least squares estimation. This paper provides an illustration of a genetic algorithm applied to a simple nonlinear least squares example.     

On one approach to the problem of robustness of density estimators

In this paper, we characterize the robustness of a density estimator by the square error increment caused by the “contamination” of a sample by a preassigned value ε. The dependence of this characteristic on ε is presented. A model example is considered. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 28–33, Perm, 1993.     

A differential game of approach with two pursuers and one evader

A differential game of approach with one evader and two pursuers with a nonconvex payoff function is considered. The duration of the game is fixed. The payoff functional is the distance between the object being pursued and the pursuer closest to it when the game terminates. An explicit form of the game value is found for all possible game positions. The paper is closely related to Refs. 1–12.The authors would like to thank Professor A. I. Subbotin for his valuable advice and encouragement.     

A genetic algorithm approach on tree-like telecommunication network design problem

In the context of telecommunication networks, the network terminals involve certain constraints that are either related with the performance of the corresponding network or with the availability of some classes of devices. In this paper, we discuss a tree-like telecommunication network design problem with the constraint limiting the number of terminals. First, this problem is formulated as a leaf-constrained minimum spanning tree (lc-MST). Then we develop a tree-based genetic representation to encode the candidate solutions of the lc-MST problem. Compared with the existing heuristic algorithm, the numerical results show the high effectiveness of the proposed GA approach on this problem.     

A new genetic approach to construct near-optimal binary search trees

Many definitive and approximate methods have been so far proposed for the construction of an optimal binary search tree. One such method is the use of evolutionary algorithms with satisfactorily improved cost efficiencies. This paper will propose a new genetic algorithm for making a near-optimal binary search tree. In this algorithm, a new greedy method is used for the crossover of chromosomes while a new way is also developed for inducing mutation in them. Practical results show a rapid and desirable convergence towards the near-optimal solution. The use of a heuristic to create not so costly chromosomes as the first offspring, the greediness of the crossover, and the application of elitism in the selection of future generation chromosomes are the most important factors leading to near-optimal solutions by the algorithm at desirably high speeds. Due to the practical results, increasing problem size does not cause any considerable difference between the solution obtained from the algorithm and exact solution.     

An alternative approach for unbalanced assignment problem via genetic algorithm

This paper presents an alternative approach using genetic algorithm to a new variant of the unbalanced assignment problem that dealing with an additional constraint on the maximum number of jobs that can be assigned to some agent(s). In this approach, genetic algorithm is also improved by introducing newly proposed initialization, crossover and mutation in such a way that the developed algorithm is capable to assign optimally all the jobs to agents. Computational results with comparative performance of the algorithm are reported for four test problems.     

On one approach to finding eigenvalue curves of linear two-parameter spectral problems

We consider an iteration algorithm for calculating eigenvalue curves of the linear algebraic twoparameter spectral problem, which uses an algorithm for calculating all eigenvalues in a given domain of variation in spectral parameters based on an efficient numerical technique for calculating derivatives of the determinant of a matrix. Numerical examples are presented.     

A new approach in studying one parameter nonlinear eigenvalue problems with constraints

The paper presents a new approach for obtaining existence and location of solutions to nonlinear eigenvalue problems depending on a parameter and subject to constraints. The location of eigensolutions and subsequent parameters is achieved by means of the graph of the derivative of a function used also to compensate the lack of coercivity. The applications concern one parameter families of semilinear elliptic eigenvalue problems with Dirichlet boundary conditions for which various qualitative properties of the solution sets are established.     

On one approach to the discretization of the lavrent’ev method

We propose a new scheme of discretization of the Lavrent’ev method for operator equations of the first kind with self-adjoint nonnegative operators of certain “smoothness.” This scheme is more economical in the sense of the amount of used discrete information as compared with traditional approaches.     

A restarting approach for the symmetric rank one update for unconstrained optimization

Two basic disadvantages of the symmetric rank one (SR1) update are that the SR1 update may not preserve positive definiteness when starting with a positive definite approximation and the SR1 update can be undefined. A simple remedy to these problems is to restart the update with the initial approximation, mostly the identity matrix, whenever these difficulties arise. However, numerical experience shows that restart with the identity matrix is not a good choice. Instead of using the identity matrix we used a positive multiple of the identity matrix. The used positive scaling factor is the optimal solution of the measure defined by the problem—maximize the determinant of the update subject to a bound of one on the largest eigenvalue. This measure is motivated by considering the volume of the symmetric difference of the two ellipsoids, which arise from the current and updated quadratic models in quasi-Newton methods. A replacement in the form of a positive multiple of the identity matrix is provided for the SR1 update when it is not positive definite or undefined. Our experiments indicate that with such simple initial scaling the possibility of an undefined update or the loss of positive definiteness for the SR1 method is avoided on all iterations.