In this paper a new method has been proposed for optimum shape **design** of **extrusion** **die**. The **Design** problem is formulated as an unconstrained **optimization** problem. Here nontraditional **optimization** techniques like **Simulated** **Annealing** **Algorithm** **and** **Particle** **Swarm** **Optimization** are used to minimize the **extrusion** force by optimizing the **extrusion** ratio **and** **die** cone angle. Internal power of deformation is also calculated **and** results are compared.

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Bose et al. (2012) investigated Reliability, Availability **and** Maintainability (RAM) characteristics of a coal based thermal power plant for finding critical subsystems **and** deciding maintenance schedule for improving availability of the plant. Khanduja et al. (2012) developed a performance model for stock preparation unit of a paper plant **using** Markov approach **and** optimize the performance **using** genetic **algorithm**. Khanduja et al. (2012) described a performance enhancement model of crystallization unit of a sugar plant **using** MA **and** GA. Kumar et al. (2012) proposed a methodology based on MA to evaluate the availability simulation model for power generation system (Turbine) of a thermal power plant under realistic working environment. The effects of occurrence of failure/course of actions **and** availability of repair facilities on system performance have been investigated. Arabi **and** Jahromi (2012) suggested a model of availability of a repairable system with multiple subsystems in which the involved components follow cold-standby strategy. The goal is to find the optimal number of repairmen **and** redundant components in each subsystem for **optimization** of steady-state availability subject to weight, cost **and** volume constraints. Due to complexity of the problem **and** time limitation, a **simulated** **annealing** **algorithm** is proposed to solve the problem.

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In 1999, for example, an amplification bandwidth of 100 nm with a ripple lower than 1 dB, **using** 12 pump lasers was demonstrated [6]. However, the complex nonlinear interaction caused by the Raman effect among pumps **and** signals over a distributed RFA employing multiple-pumps turns the adjustment of the powers **and** the wavelengths of the pumps a difficult task [3]. Because of this, some global **optimization** methods have been proposed to solve this problem such as: **simulated** **annealing** [2]; neural networks [7]; **particle** **swarm** **optimization** **and** genetic **algorithm** [8]. However, despite the conflicting nature between gain **and** ripple, to the best of our knowledge, no multi-objective **optimization** approach was applied to solve this problem so far.

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Dynamic programming is one of the techniques to solve ELD problem, but it suffer from problem of irritation of dimensionality [2]. Meta-heuristic techniques, such as genetic algorithms [3-5], differential evolution [6] , tabu search [7] ,**simulated** **annealing** [8], **particle** **swarm** **optimization** (PSO) [9], biogeography-based **optimization** [10],intelligent water drop **algorithm**[11] ,harmony search[12] ,gravitational search **algorithm**[13],firefly **algorithm**[14],hybrid gravitational search[15],cuckoo search (CS) [16],modified harmony search[17] have been successfully applied to ELD problems. Recently, a new meta-heuristic technique called grey wolf **optimization** has been proposed by Mirjalili et al., [18]. In this paper the ELD problem has been solved by **using** grey wolf **optimization**.

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We have presented a new heuristic called center-of-mass-based placing technique for packing unequal circles into a 2D circular container with additional balance constraints. The main feature of our **algorithm** is the use of the Euclidean plane with origin in the center of mass of the system to select a new circle to be placed inside the container. We evaluate our approach on a series of instances from the literature **and** compare with existing algorithms. The computational results show that our approach is competitive **and** outperforms some published methods for solving this problem. We conclude that our approach is simple, but with high performance. Future work will focus on the problem of packing spheres.

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Abstract—This paper discusses novel dedicated hardware architecture for hybrid **optimization** based on Genetic **algorithm** (GA) **and** **Simulated** **Annealing** (SA). The proposed architecture achieves high speed processing. Moreover, it achieves the searching not only globally, but also locally. To keep general purpose, self-control processing by a handshake system is introduced. By adopting the handshake system, the proposed architecture can be applied to various combinatorial **optimization** problems by only changing an encoder, a decoder, **and** an evaluation circuit. Furthermore, the proposed architecture realizes flexibility for many genetic operations on GA. In order to evaluate the proposed architecture, we conduct two kinds of experiments. One is an experiment which applies the proposed architecture to TSP, **and** the other is an experiment which applies it to VLSI floorplanning. These experiment results prove that the proposed architecture achieves high speed processing, while keeping the quality of the solutions.

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In this paper, a linear phased array is synthesized to produce a desired far field radiation pattern with a constraint on sidelobe level **and** beamwidth. The amplitude of the excitation current of each individual array element is optimized to give desired sidelobe level **and** beamwidth. A modified **particle** **swarm** **optimization** (PSO) **algorithm** with a novel inertial weight variation function **and** modified stochastic variables is used here. The performance of the modified PSO is compared with standard PSO in terms of amount of iterations required to get desired fitness value **and** convergence rate. **Using** optimized excitation amplitudes, the far field radiation pattern of the phased array is analyzed to verify whether the **design** criterions are satisfied.

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Considering only the spatial component, the stations are uniformly distributed in space. Similar results were obtained by Sacks **and** Schiller (1988) **and** Pardo-Igúzquiza (1998) for spatial models (without sampling costs in the case of the latter). The two solution sets (A1 **and** A2) have similar spatial distribution **and** correspond to geometric configurations with the same estimation variance. Had an anisotropic variogram been used, the spatial distribution would be different. One interesting feature of the data used in this case-study is that variogram models are similar in all time periods. In cases where the variogram models vary with time care is needed when identifying the period(s) when the spatial variability is best reproduced, **and** this/these is/are not necessarily when it is higher. High spatial variabilities may occur because of particular events, like extraordinary pumping regimes, **and** thus do not reflect the natural state of the system.

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A distribution network with renewable **and** fossil-based resources can be operated as a micro- grid, in autonomous or nonautonomous modes. Autonomous operation of a distribution network requires cautious planning. In this context, a detailed methodology to develop a sustainable autonomous micro-grid is presented in this paper. The proposed methodology suggests novel sizing **and** siting strategies for distributed generators **and** structural modifications for autonomous micro-grids. This paper introduces the **Particle** **Swarm** **Optimization** (PSO) **algorithm** to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new **and** powerful intelligence evolution method for solving **optimization** problems. It is a population-based approach. The PSO was inspired from natural behavior of the bees on how they find the location of most flowers. The proposed PSO **algorithm** is introduced with some modifications such as **using** an inertia weight that decreases linearly during the simulation. This setting allows the PSO to explore a large area at the start of the simulation.

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Present days high speed & quality is a major issue in communication. Lots of techniques are developed for improving the quality of signal in communication field. For proper communication, it is necessary that information is received at the receiver without any distortion. But due to the presence of noise distortion take place. For transmission of acoustic signal this distortion is not tolerable. For removing distortion it is necessary that noise will be cancelled out. Different methods are used for cancelling the noise one of the famous method is active noise control (ANC) method. In ANC method an antinoise signal is generated, whose magnitude is similar to the noise signal but its phase is opposite to the noise signal. When antinoise **and** noise signal are combined then destructive interference take place **and** noise is cancelled out. This scheme contains a reference microphone which sampled noise to be cancelled, an electronic control unit to process the input signal **and** generate control signal. This control signal is given to the loudspeaker **and** finally loudspeaker generate antinoise signal **and** antinoise signal get mixes with noise signal **and** cancelled it. If some noise is remaining out then it is treated as an error signal **and** it is absorbed by the microphone it act as feedback signal to the controller. The controller can adjust itself for generating such type of antinoise signal so that it can cancelled out noise completely **and** error become zero. Controller contain digital filter which synthesis the

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In Random based RAT selection, when a new call or vertical handover arrives, any of the available RATs will be selected randomly. In Load balancing based RAT selection, the main objective is to uniformly distribute the load among the available RATs in heterogeneous wireless networks (Tolli **and** Hakalin, 2002; Suleiman et al., 2006; Umbert et al., 2007). In policy based RAT selection, it allocates users to the RAT based on some specific rules specified by the network (Perez-Romero et al., 2005; 2006). Service class based RAT admits calls into particular RAT based on class of service such as voice, video streaming, real time video, web browsing (Zhang, 2005). Service cost based RAT admits incoming call into the least expensive RAT in order to reduce the service cost incurred by the users. Path loss based RAT selection **algorithm** makes call admission **algorithm** based on path loss measurements taken in the cells of each RAT Perez-Romero et al., 2006).

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3.6.1.1 ACO for Continuous **Optimization** Problems ACO algorithms have been extended to deal with continuous **optimization** problems. The main is- sue in adapting the ACO metaheuristic is to model a continuous nest neighborhood by a discrete structure or change the pheromone model by a continuous one. Indeed, in ACO for combinatorial problems, the pheromone trails are associated with a ﬁnite set of values related to the decisions that the ants make. This is not possible in the continuous case. ACO was ﬁrst adapted for continuous **optimization** in Ref. [75]. Further attempts to adapt ACO for continuous **optimization** were reported in many works [224,708,778]. Most of these approaches do not follow the original ACO frame- work [709]. The fundamental idea is the shift from **using** a discrete probability dis- tribution to **using** a continuous one (e.g., probability density function [709]).

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FIR digital filter can change its amplitude frequency randomly **and** can guarantee accurate linear phase at the same time, accordingly it has bright research prospect. FIR digital filter is a basic computing unit of digital signal processing[1]**and** plays an important role in communication field **and** in the processing of digital signal. The **design** core of the FIR digital filter[2]centers on the **optimization** of multidimensional variable[3]. The **design** method of FIR digital filters are mainly: window function method, Chebyshev **and** frequency sampling method etc. However, the window function method cannot properly handle transition band. The Frequency Sampling Method results in the fluctuation on the edge of passband **and** the sampling frequency is restricted to integral number of 2 π / N which cannot be sure the value of the cut-off frequency. N should be taken into consideration if need choose any value of the cut-off frequency, but this increases the amount of calculation. The newly emerged methods such as Genetic **Algorithm**(GA)[4], neural network method[5] **and** **Particle** **Swarm** **Optimization**(PSO) **algorithm**[6], although those methods do have its inspiring effects but still have serious shortcomings such as high complexity **and** slow convergence rate etc.

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Future research should also cover the **design** of heuristics for repairing unfeasible solutions, the gen- eration of an initial population, **and** the hybridization of the PSO heuristic with local search procedures. Comprehensive tests with larger **and** heterogeneous instances are required, in order to fully validate **and** tune the proposed general approach. In line with this research area, the authors are developing an application of PSO for the Vehicle **and** Crew Scheduling Problem (VCSP). The MCP model for the VCSP is likely to have a good performance, when compared with the more traditional Set Covering/Set Partitioning approaches.

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B. Results of feature selection by CPSO **and** other methods In order to evaluate the performance of the proposed method, we compare the four algorithms, Non-SVM, MC-SVM, BPSO **and** CPSO. The results in the Table II show the classification accuracy of the four algorithms. The CPSO gain the best accuracy in the six datasets when compare with the Non-SVM, MC-SVM, **and** BPSO. The below reason can explain why the CPSO outperformed BPSO in our experiment. In several generations, the classification accuracy results of PSO usually remains unchanged, indicating that PSO is stuck in a local optimum. On the other hand, CPSO still increases the classification accuracy, except for the SRBCT data set where it does not increase continuously in the later generations. However, CPSO incorporate the complementary process; therefore, it can effectively escape the local optimum.

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REAL-time object tracking is the critical task in many computer vision applications such as surveillance. Two major components can be distinguished in a typical visual tracker. Target Representation **and** Localization is mostly a bottom-up process which has also to cope with the changes in the appearance of the target [1]. Intelligent Transportation System is important application that allows for tracking of vehicles. Target tracking is often formulated as a state estimation problem where the position of the target as a function of time is considered a random process [2].The key to successful target tracking lies in the optimal extraction of useful information about the target’s state from the observations. A good model of the target will certainly facilitate this information extraction to a great extent [3].Multiple object tracking has been a challenging research topic in computer vision. It

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An experience oriented-convergence improved gravitational search **algorithm** (ECGSA) based on two new modifications, searching through the best experiments **and** **using** of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations **and** uses those as the agents’ positions in searching pro- cess. In this way, the optimal found trajectories are retained **and** the search starts from these trajectories, which allow the **algorithm** to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process **and** they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by mini- mum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed **algorithm** are compared with some well-known heuristic methods **and** verified the proposed method in both reaching to optimal solutions **and** robustness.

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As can be noted from Figure 5-14, the battery waits for the periods when the energy has less value to charge **and** stores the energy until a point of greater economic value, just like in the first model analyzed. Another conclusion that can be made from Figure 5-15 is that the battery's operation is similar to the operation for the previous model. This suggests for a business model such as that established in this chapter, the addition of a BESS to the PV does not lead to a greater economic gain compared to operating them separately. This can be attributed mainly to two factors: Firstly, PV production usually occurs at times when energy is more expensive, so it is beneficial to sell energy immediately to the grid. Second, the BESS will be able to maximize its profit if it can buy the maximum amount of energy in periods of lower prices, other than holding energy from PV to sell to sell in subsequent periods with a lesser margin.

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The process of partitioning a digital image into multiple regions (set of pixels) is called image segmentation. The partitions are different objects in image which have the same texture or color. The result of the image segmentation is a set of regions that collectively cover the entire image. All of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristics. Some of practical applications of image segmentation are: image processing, computer vision, face recognition, medical imaging, digital libraries, image **and** video retrieval [1]. Image segmentation methods fall into five categories: Pixel based segmentation [2], Region based segmentation [3], Edge based segmentation [4 5], Edge **and** region Hybrid segmentation [6] **and** Clustering based segmentation [7 8 9]. Color image segmentation **using** fuzzy classification is a pixel based segmentation method. A pixel is assigned a specific color by the fuzzy system. One approach in designing such a fuzzy system is an expert to look at training data **and** try to

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In a multiglobal **optimization** problem we aim to find all the global solutions of a constrained nonlinear programming problem where the objective function is multimodal. This class of global **optimization** problems is very important **and** frequently encountered in engineering applications, such as, process syn- thesis, **design** **and** control in chemical engineering. The most common method for solving this type of problems uses a local search method to refine a set of approximations, which are obtained by comparing objective function values at points of a predefined mesh. This type of method can be very expensive nu- merically. On the other hand, the success of local search methods depends on the starting point being at the neighbourhood of a solution. Stochastic methods are appropriate alternatives to find global solutions, in which convergence to a global solution can be guaranteed, with probability one. This is the case of the **simulated** **annealing** (SA) method. To compute the multiple solutions, a function stretching technique that transforms the objective function at each step is herein combined with SA to be able to force, step by step, convergence to each one of the required global solutions. The constraints of the problem are dealt with a penalty technique. This technique transforms the constrained problem into a sequence of unconstrained problems by penalizing the objective function when constraints are violated. Numerical experiments are shown with three penalty functions.

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