Top PDF Improved Ant Colony Optimization Algorithm based Expert System on Nephrology

Improved Ant Colony Optimization Algorithm based Expert System on Nephrology

Improved Ant Colony Optimization Algorithm based Expert System on Nephrology

Expert system Nephrology is a computer program that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. The knowledge base consists of information about a particular problem area. This information is collected from domain experts (doctors). This system mainly contains two modules one is Information System and the other is Expert Advisory system. The Information System contains the static information about different diseases and drugs in the field of Nephrology. This information system helps the patients /users to know about the problems related to kidneys. The Nephrology Advisory system helps the Patients /users to get the required and suitable advice depending on their queries. This medical expert system is developed using Java Server Pages (JSP) as front-end and MYSQL database as Backend in such a way that all the activities are carried out in a user-friendly
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A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement

A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement

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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Ant Colony Optimization Based Modified Termite Algorithm (MTA) with Efficient Stagnation Avoidance Strategy for MANETs

Ant Colony Optimization Based Modified Termite Algorithm (MTA) with Efficient Stagnation Avoidance Strategy for MANETs

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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Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm

Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm

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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Ant Colony Optimization: a literature survey

Ant Colony Optimization: a literature survey

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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Ant Colony Optimization for Capacitated Vehicle Routing Problem

Ant Colony Optimization for Capacitated Vehicle Routing Problem

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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Procedural Optimization Models for Multiobjective Flexible JSSP

Procedural Optimization Models for Multiobjective Flexible JSSP

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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On the Solutions to the Travelling Salesman Problem using Nature Inspired Computing Techniques

On the Solutions to the Travelling Salesman Problem using Nature Inspired Computing Techniques

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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An Adaptive Learning Based on Ant Colony and Collaborative Filtering

An Adaptive Learning Based on Ant Colony and Collaborative Filtering

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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AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR

AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR

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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Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

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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DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT  COLONY OPTIMIZATION

DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT COLONY OPTIMIZATION

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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DNA Motif Discovery Based on Ant Colony Optimization and Expectation Maximization

DNA Motif Discovery Based on Ant Colony Optimization and Expectation Maximization

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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FEATURE DIMENSION REDUCTION FOR EFFICIENT MEDICAL IMAGE RETRIEVAL SYSTEM USING UNIFIED FRAMEWORK

FEATURE DIMENSION REDUCTION FOR EFFICIENT MEDICAL IMAGE RETRIEVAL SYSTEM USING UNIFIED FRAMEWORK

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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A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION

A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION

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 new IPSO-SA approach for cardinality constrained portfolio optimization

A new IPSO-SA approach for cardinality constrained portfolio optimization

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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Heuristic Task Allocation Strategies for Computational Grid

Heuristic Task Allocation Strategies for Computational Grid

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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Perspective texture synthesis based on improved energy optimization.

Perspective texture synthesis based on improved energy optimization.

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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VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER

VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER

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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Ant colony optimization techniques for the hamiltonian p-median problem

Ant colony optimization techniques for the hamiltonian p-median problem

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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