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MMAS is also **based** **on** the **ant** **colony** **system**. It **based** **on** the search paradigm that is applicable for the solution of **optimization** proble m. It is an **improved** version of **ant** **system**. In **ant** **system** the disadvantage is that stagnation of search ma kes tour improve ment impossible. When stagnation occurs, the trials **on** few edges grow so fast that ants always choose the same path again and again. So MMAS a llo ws best ants to update the trails. In MMAS min imu m and ma ximu m tria l strength are introduced exp lic itly and it is depends **on** the average path length. It also provides local search heuristic. There are two ways to allo w an **ant** to perform local search, first is that to allow all ants to perform local search and second is to iteration- best.

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41 Stagnation avoidance **algorithm** was developed while ACO found its application in Scheduling of flexible manufacturing systems [6]. The methodology developed deals with quick convergence and stagnation avoidance in which parameters of ACO such as evaporation co-efficient which control the trail and its visibility are fine tuned. MMAS was further **improved** by adopting Minimum Pheromone Threshold Strategy (MPTS)[7]. Here the bound between maximum and minimum threshold is fine tuned **based** **on** performance of the network. **Improved** Lower Limits for Pheromone Trails in ACO adopts **improved** estimates of the lower pheromone value. This helps algorithms like MMAS (sets implicit pheromone trail limit) to avoid stagnation [8]. Another work **on** stagnation avoidance for scheduling of real-time tasks [9] uses a non-preemptive scheduling approach **based** **on** distance function as an extra parameter in the transition rule with the pheromone information.

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Two popular problem solving strategies are (1) Exact method – deals with logical or mathematical manner of solving problems (2) Heuristic approach [21] – deals with complex **optimization** problems which cannot be solved by traditional methods with acceptable time and space complexity. Bio-Inspired algorithms are best examples of heuristic approach. Biologically inspired algorithms has routed out from the natural behavior of organisms. It acts as source of inspiration for development of problem solving techniques. Bio-inspired computing has emerged as a new era for of providing computational solutions to complex problems in data mining [22]. The motivation to study such algorithms is **based** **on** the innovative bio-inspired solutions [5] for complex problems which cannot be done by traditional methodologies. These heuristic approaches play a vital role in various applications of web mining [23]. Categories of such bio inspired computing (inspired by nature) are (i) Swarm Intelligence (ii) Artificial Immune Systems (iii) Evolutionary Computation and (iv) Neural networks. The Swarm Intelligence includes (1) **Ant** **Colony** (2) Particle Swarm and (3) Bee **algorithm**.

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The idea behind **ant** algorithms is to adapt and use their communication style which has been proven to be so good in nature, rather than truly mimic the behaviour of real ants. Artificial ants can then be seen and described as communicating agents sharing some characteristics of the real ants, but also incorporating other characteristics for which there is no parallel in nature, (Solimanpur et al, 2004). The overall characteristics are what makes them fit to solve problems, if not optimally, at least by finding very good solutions. A real foraging **ant** spends all its life travelling between its nest and some food source. It does not then come as a surprise that the first problem solved with an **ant** **algorithm**, called **Ant** **System** (AS), was the Travelling Salesman Problem (TSP), a well-known combinatorial problem, where the shortest route (path) that visits exactly once each city of a given set of cities, starting and ending at the same city, is to be found. The very good results that were being achieved with **ant** algorithms pointed to the broadening of the definition of path therefore allowing for the use of this method to solve other problems. Some adaptations of the **algorithm** had to take place, resulting in the so called **Ant** **Colony** **Optimization** metaheuristic, which is **based** **on** the **ant** **system**. The definition of the ACO meta- heuristic, as a series of generic guidelines that could be very easily adapted to almost all types of combinatorial **optimization** problems, allowed a boost in the use of this methodology and in the number of researchers and publications in the area. Since then, ACO procedures have been applied to solve a broad set of problems, including: Network Flow Problems (Monteiro et al, 2012), Network Design Problems (Rappos and Hadjiconstantinou, 2004), Assignment Prob- lems (Shyu et al, 2006; Bernardino et al, 2009), Facility Location Problems (Baykasoglu et al, 2006; Chen and Ting, 2008), Transportation Problems (Musa et al, 2010; Santos et al, 2010), Covering Problems (Lessing et al, 2004; Crawford and Castro, 2006; Mehrabi et al, 2009), Lo- cation Problems (Pour and Nosraty, 2006), just to mention but a few in the area of combinatorial **optimization**. Curiously enough, although the TSP was the first problem to be solved by the AS and ACO metaheuristics, it still inspires researchers such as Garc´ıa-Mart´ınez et al (2007), for instance, that have recently used ACO to solve a bi-criteria TSP or Tavares and Pereira (2011) that use the TSP to test an evolving strategy to update pheromone trails.

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To apply the ACO for solving the CVRP, Voss (1999) first developed an ACO **algorithm** which is called **Ant** **System** (AS) for the problem and then presented an **improved** AS in Bullnheimer et al. (1999). Since then, many researchers have proposed new methods to improve the original ACO especially by applying other algorithms into the ACO to tackle the large-scaled CVRP. For instance, Doerner et al. (2002) proposed a hybrid approach for solving the CVRP by combining the AS with the savings **algorithm**. After that, Reimann et al. (2002) **improved** **on** the method in Doerner et al. (2002) by presenting a Savings **based** **Ant** **System** (SbAS) and then Reimann et al. (2004) proposed an approach called D-Ants which is competitive with the best Tabu Search (TS) **algorithm** in terms of solution quality and computation time. Also, Mazzeo and Loiseau (2004); Bell and McMullen (2004); Yu et al. (2009) and Zhang and Tang (2009), have made major contributions to the development of ACO to tackle the CVRP. This study aims to compare the solution quality of different basic heuristics combined with an original ACO in solving the problem.

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The other **optimization** models are named unconventional models; they place the pro- cess model **on** a secondary layer and the pri- mary role in modeling is assessed here to a procedure that controls the **system**. This pro- cedure may be represented as a sequence of instructions, most likely to transpose in algo- rithms. Therefore, the unconventional models are also named procedural models, and their great development in the last decades was sustained by the mentioned major difficulty in rigorous mathematical characterization of big complex technological processes behav- ior; in this case, it is not possible an approach which easily covers all the possible states of the **system** [13]. Most of the procedural mod- els use techniques and mechanisms specific to the biologic systems, especially represen- tation schemata and generation of behaviors, and for that reason they are included in the artificial intelligence field. By the help pro- vided by this kind of models, we try to re- place the human operator which has a “be- havior **based** **on** skills” with applications used as intelligent assistants to facilitate good decisions in less time [16]. Among the pro- cedural **optimization** models significant are: evolutionary algorithms in general and genet- ic algorithms in particular, agent-**based** mod- els (negotiation techniques, **Ant** **Colony** Op- timization, Particle Swarm **Optimization**, Wasp Behavior Model, artificial bee **colony** **algorithm**), neural networks, fuzzy tech- niques, **expert** systems and knowledge-**based** systems. The last four models were detailed in [15].

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do. They applied **Ant** **Colony** **System** (ACS) approach to various standard benchmark functions to the symmetric and asymmetric TSPs. The result obtained show that ACS finds results which are at least as good as, and often better than, those found by then available techniques. They modified ACS to deal with bigger TSPs and they reported that the modified version **improved** the performance of ACS. Dorigo and Gambardella [5] applied **ant** **colony** approach to TSP and compared the results obtained with other heuristics available in the literature at that time. They tested the **algorithm** **on** two sets of TSP problems, first one comprising five randomly generated 50-city problems, while the second one comprised of three geometric problems (real geographic problems) that consists of 50 to 100 cities. By the results obtained they concluded that the **ant** **colony** **system** almost always offered the best performance when compared with the results obtained by the existing techniques. They also modified and tested the **algorithm** for bigger TSP. This **algorithm** was able to find good results for problems up t o more than 1,500 cities. Also, the time to generate a tour grows only slightly more than linearly with the number of cities. Gambardella and Dorigo [10] hybridized **ant** **system** with Q learning and proposed **Ant**-Q algorithms. They applied this concept to get the solution for both symmetric and asymmetric instances of TSP. They performed experiments to investigate the function of **Ant**-Q. The results obtained by **Ant**-Q **on** symmetric TSP were competitive with those obtained by other heuristic approaches **based** **on** neural networks or local search. **On** the other hand results obtained by **Ant**-Q were very good when applied to some difficult asymmetric TSP's. Gambardella and Dorigo [11] developed ACS by modifying **Ant**-Q **algorithm**. They opted for a local trial updating policy to improve the performance of the **system** in term of speed and quality of results.

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Kolb [10], Felder and Silverman [6] indicated that students learn in different manners: some of them learn best when visualizing contents, others when listening…etc. Thus, a learning style can be considered as a general predisposition to process information, in particular manner. Learning styles have to be considered when elaborating the dynamic learning environment [9]. Our approach is different from many existing models and brings some contribution, especially when using together algorithms such: **Ant** colonies (adaptive courses) and filtering (similarities' treatment) for **optimization** reasons, and at the same time without reducing the Unified Learning Model Style (ULSM) interest, gathering a height number of psychological features derived from various learning style models which we find in literature. The following sections include: the literature review of the related studies (section 2), a description of our designing **system** (section 3); an introduction to **optimization** **algorithm** (section 4), some screenshots (section5), and finally, the concluding remarks of the study (section 6).

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55 increases along with number of cities. In addition changes in the mutation rate affect the performance of their approach and the quality of solutions is bias by the sizes of the populations. In [16] a hybrid method combining **ant** **colony** and beam search is proposed. It relies **on** the use of an effective local search procedure to improve previous results. In [17] an **improved** ACO is presented to solve the TSP. The proposal reduces the processing costs involved with routing of ants in the conventional ACO. The work in [1] introduces a hybrid nature inspired approach **based** **on** Honey Bees Mating **Optimization**. It successfully solves the Euclidean TSP. Finally, in [18] the authors propose four **improved** genetic algorithms using three local search methods: 2- opt search, a hybrid mutation and a combined mutation operator that are incorporated to the sequential constructive crossover.

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uring the last decades, some **optimization** algorithms, such as artificial neural network, genetic **algorithm** and artificial immune **algorithm**, have been successfully applied to solve complex **optimization** problems in ECNDT. **Ant** **colony** **algorithm** is another heuristic search **algorithm** succeeding artificial neural network, genetic **algorithm** and artificial immune **algorithm**. It is a bionic natural **optimization** **algorithm** **based** **on** research of foraging behaviors of a real **ant** **colony**. It has characteristics of probability seeking and adopts the catalytic mechanism of parallelism and positive feedback. **Ant** **colony** **algorithm** has strong robustness and an excellent distributed computing mechanism. It is easy to combine with artificial neural networks, genetic **algorithm**, artificial immune **algorithm** and particle swarm **optimization** **algorithm**. However, when solving the continuous domain **optimization** problems, **Ant** **colony** **algorithm** has the disadvantages of slow convergence and is time consuming in the process of evolution [13, 14]. This paper presents an **improved** **ant** **colony** **algorithm** (IACA) and proposes the use of IACA to quantitative estimate defect size from EC inspection signals. The IACA has more global search capability and robustness, and ease of implementation.

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The literature survey shows that many of researchers have found number of solution for detection of breast cancer with better accuracy [4]. In [6] author uses neural pattern recognition model which is the combination of two methodologies fuzzy systems and evolutionary algorithms, from which they got the success of 97%. Another method by using the hybrid **system** for diagnoses of the breast cancer **based** **on** FCOSVM represented in [3] improves the accuracy up to 97.34%. In [5] authors suggested other technique using segmentation with fuzzy models and classification by crisp k-nearest neighbor (k-nn) for breast cancer. In [7] authors shows the comparison of various methods using neural network for diagnosis of breast cancer in which the authors found that by using Jordan and Elman Network has achieved more accuracy up to 98.03%. Amin Einipour in [8] combines two methods fuzzy systems and ACO **algorithm** which automatically produce systems for breast cancer diagnosis which gives the results with accuracy 98.21%.

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Abstract—The identification of transcription factor binding sites (TFBSs) is important for understanding the genetic regulatory **system**, but weak conservation of TFBSs poses a challenge in computational biology. In this study, we propose a method **based** **on** the **Ant** **Colony** **Optimization** (ACO) and Expectation Maximization (EM) **algorithm** to discover DNA motifs (collections of TFBSs) in a set of bio-sequences. In our method, ACO builds candidate motifs to search for putative binding sites amid the given sequences. The EM **algorithm** is then applied to maximize the likelihood of a motif model being constructed from the corresponding binding sites. In ACO, each artificial **ant** mimics the foraging behavior of social insects to construct a possible motif by sensing the pheromones laid **on** each nucleotide. Due to stability issues with metaheuristic approaches, we incorporate the EM **algorithm** in our method to improve the reliability of binding site predictions. In the final step, a statistically-**based** procedure is applied to refine the predictions for compliance with real biological conditions. Experiments conducted **on** real test datasets indicate that the proposed method identifies binding sites with higher accuracy and reliability than two other motif discovery tools, namely GAME and GALF.

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The second level of CBMIR is Feature selection which would be defined as selecting the amalgamation of features between a larger feature vector database that defines a specific feature set is finest. Researchers have addressed the dimensionality reduction problem by applying various algorithms such as Principal Component Analysis, Weighted Multi-Dimensional Scaling, Tabu Search Method (Wu et al., 2009) and Evolutionary Algorithms for optimizing the features are Particle Swarm **Optimization** (PSO) (Ye et al., 2009), **Ant** **Colony** **Optimization** (ACO) (Piatrik and Izquierdo, 2009), Genetic Algorithms (GA) (Silva et al., 2011), Gravitational Search **Algorithm** (GSA) (Rashedi et al., 2009). The individual dimensional reduction approach **based** CBMIR is used to recognize the probable optimal resolutions within a sensible amount of time but the convergence rapidity is declined. PSO is one of best heuristic algorithms but the basic drawback of classic PSO is that the selection of parameters that are impulsive convergence whenever the particle and cluster finest solutions are narrowed into local minimums through the search process. A Fuzzy **based** PSO (FPSO) (Yong-Feng and Shu-Ling, 2009) approach is applied to astonish the impulsive convergence and besides to increase the speed of the penetrating process (Yogapriya and Vennila, 2013). The parameters of inertia weight and learning factors of PSO are robustly adapted using fuzzy IF/THEN rules. FPSO changes its behaviour during the **optimization** process **based** **on** information gathered at each iteration. Hence FPSO procedure would integrate a smart policymaking structure of ACO **algorithm** called FPSO-ACO (Nafar et al., 2012) where the global optimum feature position is exclusive for every particle also it would disperse different global optimum feature positions to every distinct particle agent.

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In this paper, a hybrid **optimization** **algorithm** **based** **on** GA and ACO to solve TSP is proposed and is then evaluated with some data, both random data and sample data from the library of TSP. Evolutional process of the GA together with instinct of **ant** **colony** in finding the shortest route to seek food are fully combined and formulated as new **optimization** method called GACO. Experimental studies demonstrate that with small amount of data, it shows insignificancy. But **on** the big data, it can improve the performance over both GA and ACO. In this case, the solution of the proposed hybridization method has been significantly **improved**. However, since this work only focused **on** the how to combine GA and ACO procedurally, some parameters of both sides should be set optimally to get better result and performance in the future.

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A relatively new meta-heuristic approach, where its application in portfolio **optimization** is still limited, is particle swarm **optimization** (PSO). Mous et al. (2006) compared PSO and GA which was applied to portfolio selection, Xu et al. (2007) and Gao and Chu (2010) proposed **improved** PSO algorithms for portfolio problem with transaction costs and quality constraints. Mishra et al. (2009) studied portfolio **optimization** problem from multi-objective perspective and considered four well- known multi-objective evolutionary algorithms including multi-objective PSO (MOPSO) which outperformed its counterparts for some numerical experiments. Cura (2009) presented a PSO **algorithm** to solve cardinality constrained portfolio. Compared with other meta-heuristics, PSO is simple, high speed, large scope and easy to be implemented by programs. However, there are still many issues in particle swarm, such as slow convergence during the latter search, poor precision and converging to local optimum. To overcome the above problems, a lot of revised PSO have emerged. Investors generally incline to restrict the number of assets in the portfolio and purchase just a subset of them. Therefore, cardinality is a practical constraint that has to be considered in decisions. In this paper, we address a portfolio **optimization** problem with floor, ceiling, and cardinality constraints. This problem is a mixed integer programming with quadratic objective function where traditional **optimization** methods fail to find the optimal solution, efficiently. We propose a new hybrid solution approach **based** **on** an **improved** PSO (IPSO) and a simulated annealing (SA) procedure to optimize the cardinality constrained portfolio problem. Our proposed **algorithm** takes advantage of inertia weights mechanism and constriction factor approach in updating the particle’s velocity to improve the PSO searching capabilities. In addition, an SA procedure is embedded into IPSO to improve the capacity of fine-tuning solution in the latter period of the search. Incorporating SA procedure can enhance the ability of IPSO for jumping out of the local optimum. Computational experiments **on** standard benchmark problems are carried out to assess the effectiveness of the IPSO-SA **algorithm** to solve cardinality constrained portfolio problem.

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Resource allocation in computational grid can be modeled as variant of the 0-1 multidimensional knapsack problem in [26]. This model uses knapsack problem which can find optimal mapping of task to resources. A fuzzy model, that uses fuzzy controller to learn relationship between application requirements and resource needs for specific requirement and request arriving rate [27]. Here controller continuously monitoring resource requirement **based** **on** current application request. A discussion **on** triangular pyramid scheduling (TPS) model for resource allocation in grid can be found in [29]. It uses triangular approach in which resources are decompose into three categories and it consider in-depth relationship between them in order to find better scheduling solutions. An compensation **based** strategies for grid resource allocation is **based** upon the compensation of resource loss during application execution by dynamically allocating additional resources [28]. It consist three components namely Execution time Estimator (Deadline Estimator), Performance Monitor, Resource Compensator. When request comes from user it calculates estimated application execution time using execution time estimator and send that estimated time to user. If user is agreed then it compensates resources for that job while [31] presented backpropogation (BP) neural network model for task scheduling in grid.

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Firstly, in the search phase, searching a nearest neighborhood affects the efficiency of the **optimization**. In [4], a building k- means clustering tree approach is performed. Hierarchical clustering is used to form a tree structure, to organize the input neighborhoods [8,18–19]. Starting with the root node, k-mean clustering is performed with k = 4, for all the neighboring inputs contained in that node. Then k sub-nodes are created corre- sponding to k clusters and the tree for each sub-node is built recursively. When the neighborhood numbers in a node falls below a predefined threshold (1% of the total), recursion stops. In our design, we are using a threshold less than k (recursion stops when neighboring numbers in a node falls below k). We still choose to use this approach for searching the nearest neighbor- hood. But due to that the operation is in high-dimensional space, we apply PCA technique to reduce dimensions before the input is processed. This effectively accelerates the search process. In our implementation, to maintain 95% variance, we kept a sufficient number of coefficients in the design. As for textures with 8*8 neighborhoods in RGBS (R, G, B, and Scale channels), the dimensionality is decreased rapidly from 256 to about 20–45. So through this, the synthesis process is obviously accelerated.

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Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization. Modifications and additions to ant colony optimization to solve the set partitionin[r]

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Our ACO **algorithm** constructs a complete VRP solution for the first **ant** prior to the second **ant** starting its movement, and as we mentioned in section 2, the total distribution cost (objective value of the HpMP) is computed by addition of the cost of arcs (i,j), which both customer i and j are adjacent to virtual depot in a same circuit.

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