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The paper deals with the problem of power quality and its solution. Passive or active compensators are usually used to reduce distortions of voltage and current waveforms. Active power filters seem to be the best choice if the effect of compensation is the only criterion. However, the financial cost of such a solution put the potential investors off. The paper shows that the cost could be reduced considerably if the optimization of active power filter allocation, as well as their nominal currents, is carried out. Two cases have been considered – active power filters with a classical control algorithm, which operates locally and aims at obtaining sinusoidal shape of waveforms in the point of installation, and active power filters with a modified control algorithm, which aims at reduction of waveform distortions in all nodes of an analyzed power system. Goal functions and optimization problems for both cases have been defined. [ABSTRACT FROM AUTHOR]

Abstract: The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different functional dimensions, update frequencies, and displacement strengths in different types of dynamic environments. Experimental results are reported by using the HGA and some other existing evolutionary algorithms in the literature. The results show that the HGA has better capability to track the dynamic optimum than some other existing algorithms. [Copyright &y& Elsevier]

Abstract: The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. [Copyright &y& Elsevier]

Abstract: The recent application of the diagonal dominance in the development of the optimization algorithms in the wireless sensor networks design, has been done by Yuan and Yu (2006) , extended in Yu et al. (2006) , and surveyed in Machado and Tekinay . In this paper, we will use the concept of generalized diagonal dominance, to improve the obtained results regarding the power control game, in three directions. We also discuss the applicability of such improvements. [Copyright &y& Elsevier]

Abstract: Highly reliable systems can reduce loss of money and time in practice. System reliability can be enhanced by: (i) increasing component reliabilities and/or (ii) providing redundancy at the component level. A trade-off between these two options is necessary for nonlinear-constrained reliability optimization. The problem of maximizing system reliability through component reliability choices and component redundancy is called as reliability–redundancy allocation problem, and it is a difficult but realistic nonlinear mixed-integer optimization problem. In this paper, under nonlinear constraints of weight, cost, and volume, we propose a new immune based two-phase approach to solve the reliability–redundancy allocation problem. In the first phase, an immune based algorithm (IA) is developed to solve the allocation problem, and in the second phase we present a new procedure to improve the solutions by IA. Numerical results of four benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature. [Copyright &y& Elsevier]

Abstract: In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the expectation maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better. [Copyright &y& Elsevier]

Abstract: Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. By modeling clustering as an optimization problem, first, we propose a pure harmony search-based clustering algorithm that finds near global optimal clusters within a reasonable time. Then, we hybridize K-means and harmony clustering in two ways to achieve better clustering. Experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an efficient search algorithm for determining optimal reserve capacity of a road network with toll settings. Optimization of network reserve capacity with toll settings can be formulated as a mathematical program with equilibrium constraints (MPEC) when user equilibrium flows are taken into account. A trust region subgradient projection method is proposed to efficiently solve this network design problem with global convergence. Numerical calculations are illustrated using an example network and promising results are reported. [Copyright &y& Elsevier]

Abstract: In this paper, we study a resource allocation and optimization problem arising from some production problems and other investment problems. We apply the incremental solution algorithms in highly restricted form to solve the related mathematical problem. We also apply the algorithms to concrete examples to demonstrate the process and the algorithm complexity. [Copyright &y& Elsevier]

Abstract: In this paper, Celis–Dennis–Tapia (C–D–T or CDT) subproblem approach, is given a new use and is employed to tackle the system of nonlinear equations. In the new method, the system of nonlinear equations is transferred into a constrained nonlinear programming problem, which is then solved by C–D–T subproblem algorithms. We handle a C–D–T subproblem to obtain a trial step. Some criterion, similar to that in the trust region technique, is then employed to determine whether to accept this trial point or not. In the new algorithm, an ϵ approximate solution or an locally infeasible point is obtained. In essence, constrained optimization methods are utilized to cope with the system of nonlinear equations. [Copyright &y& Elsevier]