This paper deal with a real life problem that is a company transports the raw material throughout a country. There are two steps in that. First step is finding the routes. Second one is improve the routes. To find the route the authors are using the decomposition method. For second step they applied the tabu search which is effectively solve various NP- hard problems. The Tabu search procedure is based on specific moves which attempt to improve two or three routes at each step. Here, the basic moves consist of insertions and exchanges of arcs in the graph of the problem. The combination of these moves provides interesting compound moves which make it possible to cross regions of infeasible solutions. Computational results on a set of test problems are reported, and comparisons withlower bound calculations indicate that the proposed **algorithm** rapidly gives solutions that are on average within 8% of optimality.

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All the evolutionary and swarm intelligence based algorithms are probabilistic algorithms and require common controlling parameters like population size, number of generations, elite size, etc. Besides the common control parameters, different algorithms require their own **algorithm**-specific control parameters. For example, GA uses mutation probability, crossover probability, selection operator; PSO uses inertia weight, social and cognitive parameters; ABC uses number of onlooker bees, employed bees, scout bees and limit; HS **algorithm** uses harmony memory consideration rate, pitch adjusting rate, and the number of improvisations. Similarly, the other algorithms such as ES, EP, DE, BFO, AIA, SFL, ACO, etc. need the tuning of respective **algorithm**-specific parameters. The proper tuning of the **algorithm**- specific parameters is a very crucial factor which affects the performance of the above mentioned algorithms. The improper tuning of **algorithm**-specific parameters either increases the computational effort or yields the local optimal solution. Considering this fact, Rao et al. (2011) introduced the teaching- learning-based **optimization** (TLBO) **algorithm** which does not require any **algorithm**-specific parameters. The TLBO **algorithm** requires only common controlling parameters like population size and number of generations for its working. The TLBO **algorithm** has gained wide acceptance among the **optimization** researchers (Rao, 2015).

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Abstract: In this paper, a **new** **optimization** **algorithm** called world cup **optimization** is proposed to achieve the optimal allocation of FACTS devices for maximizing the total transfer capability of power transactions between source and sink areas in a power system. World cup **optimization** **algorithm** in this study searches for the location and the size of FACTS. Multi-objective OPF with considering penalty function will be performed to solve and handle different inequality constraints. For analyzing the performance of the proposed method, it has been applied on IEEE 14 bus system. Final simulations show that the world cup **optimization** based method gives good results which may be used for online total transfer capability calculation and even the optimized method could enhance the transfer capability calculation value far more than OPF without FACTS devices.

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In this paper we present a **new** **optimization** **algorithm**, and the proposed **algorithm** operates in two phases. In the first one, multiobjective version of genetic **algorithm** is used as search engine in order to generate approximate true Pareto front. This **algorithm** is based on concept of co-evolu- tion and repair **algorithm** for handling nonlinear constraints. Also it maintains a finite-sized arc- hive of non-dominated solutions which gets iteratively updated in the presence of **new** solutions based on the concept of ε -dominance. Then, in the second stage, rough set theory is adopted as lo- cal search engine in order to improve the spread of the solutions found so far. The results, pro- vided by the proposed **algorithm** for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our **algorithm** is better applicable for solving real-world application problems.

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Hybrid harmony search and artificial bee colony **algorithm** for global **optimization** problems have introduced the artificial bee colony **algorithm** is a **new** swarm intelligence technique inspired by intelligent foraging behavior of honey bees. The ABC and its variants are used to improve harmony memory (HM). To compare and analyze the performance of hybrid algorithms, a number of experiments are carried out on a set of well known benchmark global **optimization** problems. The effects of the parameters about the hybrid algorithms are discussed by a uniform design experiment. The newly introduced on self-adaptive harmony search **algorithm**. Hybrid Harmony Search with Artificial Colony Bee **algorithm** (HHSABC) for solving global numerical **optimization** problems. The Artificial Bee Colony (ABC) **algorithm** is a **new** swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. In the ABC **algorithm**, the colony of artificial bees contains three groups of bees: employed bees, onlookers and scouts

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BFGS QN-method has a reliable and efficient performance in solving **optimization** problems for the unconstrained minimization of a smooth nonlinear function f : R n → R . However, the need to store an n x n approximate Hessian has limited their application to problems with a small to medium number of variables. For large n it is necessary to use methods that do not require the storage of a full n by n matrix. Sparse QN-updates can be applied if the Hessian has a significant number of zero entries, see for example, (Powell and Toint, 1979) and (Fletcher, 1995). In nonlinearly constrained **optimization**, other methods must be used. Such methods include CG-methods, limited-memory (LM) and QN methods, and LM reduced-Hessian QN methods (Gill and Michael., 2000).

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The Ant Colony **Optimization** **algorithm** is well known for its shortest path finding technique first proposed by M.Dorigo in 1992. The ACO **algorithm** is based on food searching behavior of ant colony. Real ants or Ants are capable of finding the shortest path from their nest to food source. Ant deposits pheromone on their path while travelling and information exchanged through environment by particular type of communication. In every ant cycle the pheromone values updated at the end of its tour. Pheromone get evaporated after certain time and calculated based on their density. Ant probably chooses the path that previously chosen by ant based on their density to find the shortest path. Pheromones get updated by the ant if it chooses the same path and improves the pheromone density. The ACO **algorithm** is a multi-agent approach for solving Combinatorial **Optimization** problems, searching problem and decision problem to find optimal solution [9].

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Engineer School of Tunis, Belvedere BP 37, Tunis 1002, Tunisia Abstract—Recently, the use of the particle swarm **optimization** (PSO) technique for the reconstruction of microwave images has received increasing interest from the **optimization** community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO **algorithm** is easily trapping into local minimum and may lead to the premature convergence. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To overcome the premature convergence of PSO, we propose a **new** hybrid **algorithm** of particle swarm **optimization** (PSO), simulated annealing (SA) and tabu search **algorithm** (TS) for solving the scattering inverse problem. The incorporation of tabu search (TS) and simulated annealing (SA) as local improvement approaches enable the hybrid **algorithm** to overleap local optima and intensify its search ability in local regions. Reconstructions of dielectric scatterers from experimental inverse- scattering data are finally presented to demonstrate the accuracy and efficiency of the hybrid technique.

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Dynamic source Routing Protocol is an important protocol used for the static routes in the network. Dynamic Routing Protocol used to facilitate the routing data between the routers. In routing protocol, the routers read the data remote networks and automatically add the data in the routing tables. The best path is selected from the routing network and then routing table is added to the network. The main advantage of the dynamic routing protocol is that, when the topology changes then routing data is exchanged between the routers. The exchange of data between the routers found the **new** network path in case there is link failure in the network. There is no overhead take place in routing protocols. Dynamic Routing play a better role than static routing [14]. The Routing Protocol is set of procedure, algorithms and messages which are used exchange routing data and then route to routing table. The trading of information between the switches found the **new** system way on the off chance that there is connect disappointment in the system for networking. Overhead occur in steering conventions. Dynamic Routing assume a superior job than static directing [15].

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This section describes the recent work reported on TLBO **algorithm**. Several studies have been published on the modifications of TLBO **algorithm**. Some of these are highlighted in this section. To make the effective tradeoff between exploration and exploitation capabilities, Rao et al. have developed an improved TLBO **algorithm**, called I-TLBO [25]. In this work, authors have introduced the concept of multiple teachers, adaptive teaching factor, self-motivated learning and tutorial training. The self learning and tutorial training methods can be acted as search methods. Further, to explore the local optimum solution in the hope of global optimum solution. The concept of multiple teachers is incorporated in TLBO **algorithm** to avoid premature convergence. Moreover, adaptive teaching factor is inculcated for fine tuning between exploration and exploitation capabilities. From results, it is seen that I-TLBO effectively overcome the aforementioned problems. Satapathy et al., have presented a **new** version of TLBO **algorithm**, called

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This paper aims to introduce a **new** metaheuristic : The Water-Tank Fish **Algorithm**, modeled after the workings of the swim bladder in fish, to non-deterministically compute the optima for numerical op- timization problems. To balance the explorative-exploitative behav- ior of a search, the proposed method uses a search localization rou- tine which, after a general exploration, restricts the search to cer- tain areas of the graph and intensifies it as the **algorithm** advances. The proposed method is tested over 40 benchmark mathemati- cal functions and the results were found to be very encouraging.

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Particle swarm **optimization** (PSO) [5] is a relatively **new** **optimization** technique, which is similar to the GA **algorithm** in the computational method, yet it is still different from GA that the PSO **algorithm** does not use the factors utilized in evolutionary computation, such as hybridization and mutation. It was inspired by the social behaviour of the birds and proposed by biologist Frank Heppner's according to his biota model. It uses non-volume massless particles as individuals, and provides simple rules of social behaviour for each particle, and obtains the search of the optimal solution of problems by collaboration of individuals among populations. Since the **algorithm** convergence is fast, the amount of parameters is low and the implementation is easy, it can effectively solve the complex **optimization** problem. GA is widely used in function **optimization**, neural network training, graphic processing, pattern recognition and some engineering fields.

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Several modern heuristic tools that facilitate the solution of **optimization** problems which were previously difficult or impossible to address have evolved in the last two decades. These tools include evolutionary computation, simulated annealing, tabu search, and particle swarm, among others. Recently, the genetic **algorithm** (GA) and particle swarm **optimization** (PSO) techniques emerged as promising algorithms for handling **optimization** problems. With the development of artificial intelligence in recent years, some approaches have been presented to using ANNs with a back propagation(BP) **algorithm** [2], Genetic **Algorithm** (GA) [2, 3- 5] and Particle Swarm **Optimization** (PSO) [4, 5-6] methods. Back propagation is a gradient-based method. Although the BP **algorithm** has solved a number of practical problems, but firstly it easily gets trapped in local minima especially for complex function approximation problem, so that back propagation may lead to failure in finding a global optimal solution. Second, the convergent speed of the BP **algorithm** is too slow even if the learning goal, a given termination error, can be achieved.

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ABSTRACT: The project is to improve the segmentation of the color satellite images. In this proposed method the color satellite image can be segmented by using Tsallis entropy and Granular computing methods with the help of Cuckoo search **algorithm**. The Tsallis and Granular computing methods will used to find the maximum possibility of threshold limits and the Cuckoo search will find the optimized threshold values based on threshold limit. The feasibility of the proposed Cuckoo search and Tsallis entropy based approach was tested on different satellite images and bench marked with differential evolution and solving the multilevel colored image thresholding problems. The multilevel thresholding will be used for the segmentation of color satellite images. By using these Cuckoo search **algorithm** experimental results will help to select the optimized threshold values for multilevel thresholding effectively and properly.

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The result of the peak load regulation of Three Gorges Cascade by adopting HPSO **algorithm** is as shown in Table 2. The peak load regulating operation process of the typical plan 3 and 5 are as shown in Fig. 2 and Fig. 3 respectively, where, horizontal axis stands for operation period and vertical axis stands for output of the cascade hydropower stations. From Table 2, the results can be seen that the load peak and off-peak difference of power system decrease 1723.78 GW by Three Gorges cascade hydropower stations, and Three Gorges stations are able to provide peaking output 1950.03 GW greatly. Fig. 2 shows that the highest system load can be cut down greatly, the major peak of residual load process is smooth obviously and the secondary peak has different degrees of weakening too. Therefore Three Gorges and Gezhouba stations have strong peaking capacity during winter season.

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During generation of individuals, each character of a chromosome in the population is mapped to an input of a net of a circuit. So a binary code is utilized and the chromosome represents a test vector. The initial population is generated randomly. AGA is composed of populations of chromosomes and three evolutionary operators: selection, crossover and mutation. The selection scheme in the paper is binary tournament selection without replacement, where two individuals are selected by the roulette wheel approach, and the better individual is selected from the two. After two chromosomes are selected, the crossover operator is employed to generate two offspring. We use the uniform crossover scheme, where each chromosome position is crossed with an adaptive probability. As the **new** individuals are generated, each character is mutated with an adaptive rate. In the binary code, mutation is done by flipping a bit.

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To reduce the computational burden for classification and approximation of the GPS GDOP, Simon and El-Sherief have used the basic back propagation (BP) approach to train the neural network (NN) [6]. Although the BP is the most popular **algorithm** to train an NN, it has two important problems: 1) the BP training is very slow in many applications including the GPS GDOP classification; and 2) the BP easily falls in local minima. To overcome these problems, Jwo and Lai [1] suggested utilizing the BP with momentum to train the NN (BPNN), the optimal interpolative network, general regression NN (GRNN), and probabilistic NN (PNN). In [5] and [7] Azami et al. proposed to use some improved NN algorithms, namely, BP with adaptive learning rate and momentum, Fletcher-Reeves conjugate gradient **algorithm** (CGA), Polak- Ribikre CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), Levenberg-Marquardt (LM), modified LM, one step secant (OSS) and quasi-Newton. In addition, to have uncorrelated and informative features of the GPS GDOP, principal component analysis (PCA) was used as a pre- processing step [8].

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collapse point [10]. The reactive power support and voltage problems are intrinsically related. Hence, this paper formulates the reactive power dispatch as a multi-objective **optimization** problem with loss minimization and maximization of static voltage stability margin (SVSM) as the objectives. Voltage stability evaluation using modal analysis [10] is used as the indicator of voltage stability. Natural selection tends to eliminate animals with poor foraging strategies and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations poor foraging strategies are either eliminated or shaped into good ones. Based on the researches on the foraging behavior of E-coli bacteria K.M. Passino proposed a **new** Evolutionary computation technique known as Bacterial Foraging **Optimization** **Algorithm** (BFOA) [11], briefly explained in the following sections. However, during the process of chemo taxis, the BFOA depends on random search directions which may lead to delay in reaching global solution. In order to speed the convergence of Bacterial Foraging **Optimization** W. Karoni had proposed an improved BFOA namely BF-PSO [12]. The BF-PSO **algorithm** borrowed the ideas of velocity updating from particle swarm **optimization** (PSO), the search directions specified by the tumble of bacteria are oriented by the individual best location and global best locations concurrently. To reduce the time of **optimization** and to accelerate the convergence speed of group of bacteria near global optima for this BFO-PSO we propose a **new** hybrid **algorithm** "ABF-PSO" in which the chemo tactic step had been made adaptive. The performance of (ABF-PSO) has been evaluated in standard IEEE 30 bus test system and the results analysis shows that our proposed approach outperforms all approaches investigated in this paper. The performance of (ABF-PSO) has been evaluated in standard IEEE 30 bus test system and the results analysis shows that our proposed approach outperforms all approaches investigated in this paper.

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to develop ways in which the mechanisms of natural adaptation might be utilized into computer systems. Holland (1975) in in Natural and Artificial Systems’ presented the GA as an abstraction of biological evolution and gave a theoretical framework for adaptation under the GA. Many problems in engineering and related areas require the simultaneous genetic **optimization** for a number of possibly competing objectives. These have been solved by combining the multiple objectives in to single scalar by the approach of linear combination. The combining coefficients, however, usually based on heuristic or guesswork can exert an unknown influence on the outcome of the **optimization**. A more satisfactory approach is to use the notion of Pareto optimality by Goldberg (1989) in which an optimal set of solutions prescribe some surface ‘The Pareto front’ in the vector space of Goldberg, 1989). For a solution on the Pareto front no objective can be improved without simultaneously degrading at least one other. This is routinely used to generate useful solutions to **optimization** and search problems. Genetic larger class of evolutionary algorithms (EA), generates solution to **optimization** problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In a genetic **algorithm**, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an **optimization** problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0’s and 1’s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every INTERNATIONAL JOURNAL OF CURRENT RESEARCH

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et al. (2014) proposed a Modified artificial bee colony (mcABC) **algorithm** for constrained **optimization** problems. mcABC proposed where three **new** solution search equations are introduced respectively to employed bee, onlooker bee and scout bee phases. Noorazliza Sulaiman et al. (2014) introduced a two **new** modified ABC algorithms referred to as JA-ABC3, JA-ABC4 with the objectives to diligently avoid premature convergence and enhance convergence speed for reactive power **optimization**. Wei Gao et al. (2014) improved ABC **algorithm** and proposed improved artificial bee colony **algorithm** based gravity matching navigation method. Bai Li et al. (2014) proposed a novel artificial bee colony (ABC) **algorithm** by a balance- evolution strategy (BES) is applied for **optimization**. AlkJn Yurtkuran and Erdal Emel (2014) introduced a modified ABC **algorithm** that benefits from a variety of search strategies to balance exploration and exploitation. Xiu Zhang et al. (2014) modified Artificial Bee Colony **Algorithm** is to promote the convergence rate of ABC which is applied to loudspeaker design problem. Noorazliza Sulaiman et al. (2014) proposed a **new** ABC variant referred as JA-ABC2 to enhance convergence rate and to avoid local optima trapping. Zhenyue Zhang et al. (2014) modified ABC that improves local search mechanism and applied to face recognition and sparse representation. Sandeep Kumar et al. (2014) introduces a local search mechanism in ABC called Enhanced local search in ABC (EnABC) that increases exploration capability of ABC and avoids the dilemma of stagnation. Shimpi Singh jadon et al. (2014) proposed an Expidited Artificial Bee Colony(EABC) to improve ABC **algorithm** by balancing its exploration and exploitation capabilities.

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