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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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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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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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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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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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 **based**” **approach** 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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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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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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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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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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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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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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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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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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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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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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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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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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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.