Top PDF A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix

A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix

A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix

Today’s rapidly changing business environment requires corporations to continuously evaluate and configure their supply chains (SCs) to provide customers with high quality products/services at the lowest possible cost and within the shortest possible time[1][2]. A supply chain is a network of optional resources through which materials (raw materials, work in progress, and finished products) flow along one direction while information (demand data, due date, delivery and assembly cost and time) along both directions in order to meet demands from customers[3]. The process of finding the best flow patterns (i.e., choices of resources) for every product in a product mix is known as the optimisation of the SC design[4][5]. When a manufacturer decides from which supplier to get each of the required components and in which manufacturing plant each of the sub-assemblies and final products must be assembled, the who-serves- whom relationships for the supply chain are established. As a consequence, the flow patterns for every product are determined. There may be multiple suppliers that could supply the same component as well as optional manufacturing plants that could assemble the same sub- assembly or product, each differentiated by a lead-time and cost. Given all the possible options for resources, the supply chain configuration problem is to select the options that minimise the total cost while keeping the total time as short as possible (or within what customers are prepared to accept).
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Multiobjective design of induction motor using ant colony optimization

Multiobjective design of induction motor using ant colony optimization

the second one for 30 kW, 400 V, 6 pole, 50 Hz. The usefulness of the PM is illustrated through comparing the performances with that of the GA based design approach. In this regard, the same set of primary design variables, cost function and design equations, involved in the PM, are used to develop the GA based design approach. The software packages are developed in Matlab platform and executed in a 2.67 GHz Intel core-i5 personal computer. There is no assurance that different executions of the developed design programs converge to the same design due to the stochastic nature of the GA and HSO and hence the algorithms are run 20 times for each IM and the best ones are presented.
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An improved FPGA routing approach based on the ant colony optimization

An improved FPGA routing approach based on the ant colony optimization

The routing of integrated circuits is an important procedure of the physical design after the layout. The layout has decided the location of modules on chips and that of pins on modules, and provided the connection information among pins through the net-list. The routing is actually to link different modules [1]. It contains two stages, namely global routing that aims to find a routing channel for each net and detail routing that aims to distribute practical channels and holes for each net [2-3]. Global routing will reasonably distribute different parts of each net to different routing channels and specifically define routing problems in each routing channel [4]. Then, routing of channels will be completed by channel routers (a kind of algorithm) during the detail routing. The routing of FPGA is a kind of global routing and the paper only focuses on existing algorithms for global routing.
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A multi-objective robust optimization model for location-allocation decisions in two-stage supply chain network and solving it with non-dominated sorting ant colony optimization

A multi-objective robust optimization model for location-allocation decisions in two-stage supply chain network and solving it with non-dominated sorting ant colony optimization

Ordonez and Zhao (2007) [10] investigated the robust capacity expansion problem of network ows under demand and travel time uncertainty. They provided complexity results for the two-stage network ow and design problem. Further, the problem of locating a competitive facility in the plane in the presence of uncertain demand was studied in [11] with a deviation robustness criterion. Baron et al. (2011) [12] applied robust optimization to the problem of locating facilities in a network facing uncertain demand over multiple periods. They considered a multi-period xed-charge network location problem for which they show that dierent models of uncertainty lead to very dierent solution network topologies, with the model with box uncertainty set opening fewer, larger facilities. Gabrel et al. (2011) [13] investigated a robust version of the location transportation problem with an uncertain demand using a two-stage formulation. The resulting robust formulation is a convex (nonlinear) program, and the authors apply a cutting plane algorithm to solve the problem exactly. Gulpinar et al. (2013) [14] considered a stochastic facility location problem in which multiple capacitated facilities serve customers with a single product, with uncertain customer de- mand and a constraint on the stock-out probability. Ghahtarani and Naja (2013) [15] proposed a robust optimization model for the multi-objective portfolio selection problem that uses a Goal Programming (GP) approach.
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Cognitive Ant colony optimization: A new framework in swarm intelligence

Cognitive Ant colony optimization: A new framework in swarm intelligence

There are a considerable number of researchers, mainly biologists, who study the behaviour of ants in detail. Biologists have shown experimentally that it is possible for certain ant species to find the shortest paths in foraging for food from a nest by exploiting communication based only on pheromones, an odorous chemical substance that ants can deposit and smell. This behavioural pattern has inspired computer scientists to develop algorithms for the solution of optimization problems. The first attempts in this direction appeared in the early 1990s, indicating the general validity of the approach. Ant Colony Optimization (ACO) algorithms are the most successful and widely recognized algorithmic techniques based on ant behaviours. These algorithms have been applied to numerous problems; moreover, for many problems ACO algorithms are among the current high performing algorithms.
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Stimulated Particle Swarm Optimization Routing
          Protocol – A Review

Stimulated Particle Swarm Optimization Routing Protocol – A Review

Mobile Ad Hoc Networks (MANET) is self-organizing network consisting of a collection of radio transceivers with no centralized infrastructures. The multicasting is a fundamental problem in MANETs where one node is required to transmit data to a subset of other network nodes. With the growing demand to support multimedia applications in wireless network, it is desirable that such network maintain multicast connection for the entire session. Energy efficiency is crucial for the implementation of multicast services in wireless ad hoc networks due to the limited battery life. The depletion of energy of a node results in disjoint network and consequently loss of connectivity. Thus energy guarding is crucial for all network operation. In the paper investigates the multicast routing in mobile ad hoc networks with the goal of minimizing the total transmitted power of all nodes in the multicast tree. The minimum energy multicast (MEM) problem is NP-complete [1] and can be stated as a combinatorial optimization problem. The existing solutions for MEM problem are mainly based on heuristic methods and a detailed survey on the same can be found in [2]. Proposed a sub-optimal greedy heuristic referred as Broadcast Incremental Power (BIP) algorithm for constructing minimum energy multicast trees in wireless networks. It is a “node basedapproach and new nodes are added to the tree on a minimum incremental costbasis until all intended destination nodes are included. The algorithm takes into account the wireless broadcast advantage. The broadcast tree is pruned to obtain the multiple trees. Another rtechniques that have been suggested include a simulated annealing procedure by Montemanniet. All [6], Swarm based ant colony approach in [7] [8]. In the Ant Colony System (ACS) approach by Das et. al. [7] the
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A Novel Approach To Solve Tsp Using Ant Colony Optimization

A Novel Approach To Solve Tsp Using Ant Colony Optimization

In [2] authors used multiobjective EA, i.e., MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D- ACO decomposes a multiobjective optimization problem into a number of single-objective optimization problems. Each ant (i.e., agent) is responsible for solving one sub problem. All the ants are divided into a few groups, and each ant has several neighboring ants .An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix [3]. Authors used a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm .In [ 4] Heterogeneous Ant Colony Optimization (HACO) algorithm to solve the global path planning problem for autonomous mobile robot in the previous paper. The HACO algorithm was modified and optimized to solve the global path planning problem unlike the conventional ACO algorithm which was proposed to solve the Traveling Salesman Problem (TSP) or Quadratic Assignment Problem (QAP). In [5] authors improved efficient programming approach for solving the proposed problem with an analogy, the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant colony optimization technique. The existing proposal is a single processor-scheduling problem in which the sum of values of all jobs is maximized and also presents a new pheromone updating strategy which is used to optimize ACO (Ant Colony Optimization) technique in solving the Traveling Salesman Problem. The value of a job is characterized by a stepwise no increasing function with one or more moments at which the changes of job value occur.In [6] Author used Multi-Objective concept to solve the Traveling Salesman Problem (TSP). The traveling salesman problem is defined as an NP-hard problem. The resolution of this kind of problem is based firstly on exact methods and after that is based on single objective based methods as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Firstly, a short description of the Multi- objective Particles swarm optimization (MOPSO) is given as an efficient technique to use for many real problems.In [7] authors considered two new variants of AS-PSO (Ant Supervised by Particle Swarm optimization) meta-heuristic are proposed and applied to a classical travelling salesman benchmark problem. The new variants are Fuzzy-AS-PSO and Simplified AS-PSO (S-AS-PSO). AS-PSO is a hierarchical meta-heuristic based on the ant colony optimisation (ACO) and particle swarm optimization (PSO), in which ACO is the heuristic and PSO is the meta-heuristic.
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OPTIMIZATION OF SEWER DESIGN USING ANT COLONY OPTIMIZATION

OPTIMIZATION OF SEWER DESIGN USING ANT COLONY OPTIMIZATION

does not incorporate commercially available diameters, there is no guarantee of optimality for standard commercial pipe diameters. The topic of optimal sewer design has been studied by many researchers. Its concept was first proposed in the mid 1960s [3,4] when advances in the computer power shined light on engineering research. Various early optimization techniques were developed, including Linear Programming (LP) [5,3], Non-linear Programming (NLP) [4], and Dynamic Programming (DP) [6]. Recently, Dorigo et al. (1996) proposed a new evolutionary optimization method, namely the ant algorithm, based on the collective behavior of the ants in their search for food. Ant algorithms were first proposed for the solution of difficult combinatorial optimization problems like TSP and QAP. This method has been shown to outperform other evolutionary optimization methods including Genetic Algorithms (GA).
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An Efficient Irrigation System with Environmental Data Analysis Using SVM Techniques

An Efficient Irrigation System with Environmental Data Analysis Using SVM Techniques

Eugen Feller et al had proposed a model in which the workload placement problem is considered as an instance of the multi-dimensional bin-packing (MDBP) problem and design a different, nature inspired algorithm based on the Ant Colony Optimization (ACO) meta-heuristic to figure the placements dynamically, according to the current workload.This is the first work to apply Ant Colony Optimization on the MDBP problem in the context of dynamic workload placement and applyACO in order to conserve energy. Similarly Xiao-Fang et al proposed an approach[19] based on Ant Colony Optimization for efficient VM Placement namely ACO-VMC to efficiently use the physical resource and reduce the number of active physical servers and thus reduce the power consumed in data centers.GaochaoXu et al proposed distributed and parallel ACO algorithm [9] namely DPACO that is executed on several physical servers to get a better solution by increasing the iterative times for the large scale VMs live migration problem. Here migration failures are easily detected since the algorithm runs distributedly and parallely on all hosts.
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Title: Ant Colony Optimization Based Approach for the Detection of Black Hole Attack in WANET

Title: Ant Colony Optimization Based Approach for the Detection of Black Hole Attack in WANET

WANET, mutually with the behind information regarding network situation, is called a Routing Protocol. Wireless communication is a rising new technology that allows the users to access information and services automatically despite of their biological position. However, similar to other networks, WANET also vulnerable to many security attacks. WANET not only inherits all the security threats faced in both wired and wireless networks, but it also introduces security attacks unique to itself. In WANET, security is a challenging issue due to the vulnerabilities that are associated with it [4].
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ANT COLONY OPTIMIZATION

ANT COLONY OPTIMIZATION

And it is made possible by an indirect form of communication known as stigmergy. Stigmergy is a particular form of indirect communication (mediated by local modifications in the environment) used by social insects to coordinate their activities (see [3], [4]). By exploiting the stigmergic approach to coordination, researchers have been able to design a number of successful algorithms in such diverse application fields as combinatorial optimization, routing in communication networks, graph drawing and partitioning, and so on (see [6]). Argentine ants, while going from the nest to the food source and vice versa, deposit a chemical substance known as pheromone, on the ground (see [3]). Now, when they arrive at a decision point that is at a point, then they make a probabilistic choice which is further based on the amount of pheromone they smell on the branches. This behavior has an autocatalytic effect because of the very fact that choosing a path will increase its probability that it will be chosen again by the future ants as they will increase the amount of pheromone on the path which is used by them.
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Secure Checkpointing Approach on Ant Colony Optimization in MANET

Secure Checkpointing Approach on Ant Colony Optimization in MANET

A mobile network is a dynamic reconfigurable network with heavy traffic over the network. In such network the optimization of QOS is always the basic need of the network. Checkpoints Oriented routing algorithm is suggested in this work. As the name suggest, during the long distance communication if some failure occur, the complete communication will be performed again. In such case, to avoid the recommunication, a check point based approach is suggested in this work. The work is divided in two main stages, in first stage, ACO will be implemented to identify the optimized route over the network. The route identification will be based on different parametric analysis such as PDR ratio of nodes, response time analysis etc. Once the route will be identified, the next work is to identify the critical location where the route diversion is possible. On these diversion points the checkpoints will be defined. As the communication will be performed, these checkpoints will perform the communication analysis like an agent. If some attack or the data loss occur, the check points are responsible to call the ACO routing again to generate the new path from that checkpoint onwards. As the work is checkpointing based so that no need to regenerate the whole route again. In this proposed work we are providing Checkpoints improved ACO routing algorithm to handle the link failure by and providing the substitute path. These substitute links are estimated initially by the help of some Checkpoints link. The Checkpoints link is place in between the route dynamically such that it will enhance the QOS over the network.
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Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem   Pages 111-132
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Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem Pages 111-132 Download PDF

assumptions and proposed a branch and bound method for its resolution. Later on, Lim et al. (2004) studied a more realistic version of the QCSP with complete bays by considering the non-interference constraints. Three approaches were proposed to determine the best schedule of quay cranes; a dynamic programming algorithm was addressed to solve simpler instances, where a probabilistic Tabu Search and a Squeaky Wheel Optimization heuristic were used to tackle the hardest instances. Zhu and Lim (2006), addressed the QCSP to minimize the latest compilation time of all tasks and considered that tasks are non-preemptive. They showed that the problem is NP-complete and provided a branch and bound algorithm and a simulated annealing approach to solve small and large instances of the problem. Lim et al. (2007) proposed a new formulation for the QCSP with complete bays and showed that there is always an optimal solution of the problem among all possible unidirectional schedules of cranes. A simple approximation heuristic and simulated annealing heuristic was designed to solve the problem. Lee et al. (2008) provided another proof of NP-completeness of the QCSP with complete bays, and proposed an efficient genetic algorithm to address the problem. The efficiency of the genetic algorithm was tested on forty random instances with large sizes and the experimental results showed that the Genetic Algorithm is very efficient since deviation to the lower bound was less than 0.9% on all instances.
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A Hybrid Multi-Agent Routing Algorithm Based on Ant Colony Optimization for MANET

A Hybrid Multi-Agent Routing Algorithm Based on Ant Colony Optimization for MANET

Network is one of the most innovative and challenging area of wireless networks. The goal of every routing algorithm is to direct traffic from source to destination, maximizing network performance while minimizing cost. In this paper, we will present a new approach for an Ad hoc routing algorithm, which is based on Ant Colony Optimization (ACO) algorithm and its combination with proactive and reactive algorithms. The basic idea of the ACO meta- heuristic is taken from the food searching behavior of real ants. While walking, ants deposit pheromone, which marks the route taken as they move from a food source to their nest, and foragers follow such pheromone trails. The concentration of pheromone on a certain path is an indication of its usage. These pheromone trails are used as a simple indirect form of communication. The process of emerging global information from local actions through small, independent agents not communicating with each other is called Stigmergy 11 . This behavior of the ants
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ANT COLONY OPTIMIZATION: A NEW APPROACH FOR ECONOMIC LOAD DISPATCH

ANT COLONY OPTIMIZATION: A NEW APPROACH FOR ECONOMIC LOAD DISPATCH

1) Inequality Constraints: These are units operational constraints, each generating unis have lower (Pi min) and upper (Pi max) generation limits, which are directly related to the design of the machine. Theses bounds can be defined as a pair of inequality constraints, as follows:

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Improving Mix-CLAHE with ACO for Clearer Oceanic Images

Improving Mix-CLAHE with ACO for Clearer Oceanic Images

Abstract— Oceanic pictures have poor visibility attributable to various factors; weather disturbance, particles in water, lightweight frames and water movement which results in degraded and low contrast pictures of underwater. Visibility restoration refers to varied ways in which aim to decline and remove the degradation that have occurred whereas the digital image has been obtained. The probabilistic Ant Colony Optimization (ACO) approach is presented to solve the problem of designing an optimal route for hard combinatorial problems. It’s found that almost all of the prevailing researchers have neglected several problems i.e. no technique is correct for various reasonably circumstances. the prevailing strategies have neglected the utilization of hymenopter colony optimization to cut back the noise and uneven illuminate downside. The main objective of this paper is to judge the performance of ANT colony optimization primarily based haze removal over the obtainable MIX-CLAHE (Contrast Limited adaptive histogram Equalization) technique. The experiment has clearly showed the effectiveness of the projected technique over the obtainable strategies.
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A Modified Ant Colony Optimizer to Solve a Variant of Dynamic TSP

A Modified Ant Colony Optimizer to Solve a Variant of Dynamic TSP

Ant Colony Optimization algorithms are computational models motivated by the collective searching for food behavior of ants. The ACO algorithm is inspired by the behavior of real ants. In order to apply the ACO met heuristic to any interesting combinatorial optimization problems; we have to map the considered problem to a demonstration that can be used by the fake ants to form a solution. Ant algorithms were motivated by the observation of real ant colonies. Ants are social pests, that is, insects that live in colonies and whose behavior is focused more to the existence of the colony as a whole than to that of a single separate component of the colony. Interesting behavior of ant colonies is their searching for food behavior, and, in specific, how ants can find nearest paths between food sources and their nest.
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Ant colony optimization based founder sequence reconstruction

Ant colony optimization based founder sequence reconstruction

Founder gene sequence reconstruction (FSR) for a given population can be modeled as a combinatorial optimization problem, which has been proven NP-hard. In this paper we propose a novel method based on ant colony optimization algorithms (ACO) coupled with two other important improvements (i.e. local search and back forward search) to solve the founder gene sequence reconstruction problem. Experiments on the benchmark data sets show better or equal results for almost sets when comparing to the best corresponding method, demonstrating the efficacy and future perspectives of our proposed method.
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Survey on Ant Colony Optimization
                 

Survey on Ant Colony Optimization  

range that acquires bulk of time accompanied with a large search space. Consequently, their performance can be enhanced by using some Meta heuristic algorithms in an attempt to shrink the search space. Some of the Meta heuristic algorithms are particle swarm optimization, ant colony optimization and genetic algorithm. ACO unearth a large number of applications, especially problems which are NP- Hard. As NP-Hard problems have an exponential worst case complexity [6], so ACO can be pooled with them to trim down the complexity to polynomial time which can further be improved if it is parallelized. There are an assortment of areas where ACO algorithms are applied like in routing, assignment problems, scheduling problems [7], data mining and classification [8], emergency path rescue [9] and wireless sensor networks [10]. This survey will be confined to only a handful of these problems therefore restricting our discussion to those.
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Ant Colony Optimization Algorithm

Ant Colony Optimization Algorithm

Abstract -Hybrid algorithm is proposed to solve combinatorial optimization problem by using Ant Colony and Genetic programming algorithms. Evolutionary process of Ant Colony Optimization algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.

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