SWARM-TECHNOLOGY-RESEARCH PAPER-SOFTWARE


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Swarm intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. Swarm Technology's strategically important issued patents define plug-and-play intent-based processing and heterogeneous interoperability in swarm robotics. Swarm Technology's architecture allows dynamic addition of processors, enables intent-based heterogeneous processing and intent-based swarm robotics.

An idea based on honey beeswarmfor numerical optimization
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Swarmintelligence has become a research interest to many research scientists of related fields in recent years. Bonabeau has defined theswarmintelligence asany attempt to design algorithms or distributed problem-solving devices inspired by the collective

Swarmintelligence
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Swarmintelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of aswarmsuggests multiplicity, stochasticity, randomness, and messiness, and the concept of intelligence suggests that the

Theswarmsimulation system: A toolkit for building multi-agent simulations
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AbstractSwarmis a multi-agent software platform for the simulation of complex adaptive systems. In theSwarmsystem the basic unit of simulation is theswarm a collection of agents executing a schedule of actions.Swarmsupports hierarchical modeling approaches

Multi-objective particleswarmoptimizers: A survey of the state-of-the-art
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Abstract: The success of the ParticleSwarmOptimization (PSO) algorithm as a single- objective optimizer (mainly when dealing with continuous search spaces) has motivated researchers to extend the use of this bio-inspired technique to other areas. One of them is

A diversity-guided particleswarmoptimizer-the ARPSO
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Abstract The particleswarmoptimization (PSO) algorithm is a new population based search strategy, which has exhibited good performance on well-known numerical test problems. However, on strongly multi-modal test problems the PSO tends to suffer from premature

Swarmintelligence in optimization
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Summary. Optimization techniques inspired byswarmintelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of

Particleswarmoptimization
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Summary Previous determination of the initial 3D zero-offset (ZO) CRS attributes was steered by the pragmatic three-step grid search. However, this search approach applies a grid search strategy at each step, which makes this method very expensive while increasing

Swarmintelligence: A whole new way to think about business
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Think About Business may 2001 107 little more than a year ago, Southwest Airlines was having trouble with its cargo operations. Even though the average plane was using only 7% of its cargo space, at some airports there wasnt enough capacity to accommodate

Adapting particleswarmoptimization to dynamic environments
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Abstract In this paper the authors propose a method for adapting the particleswarm optimizer for dynamic environments. The process consists of causing each particle to reset its record of its best position as the environment changes, to avoid making direction and

A niching particleswarmoptimizer
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ABSTRACT This paper describes a technique that extends the unimodal particleswarm optimizer to efficiently locate multiple optimal solutions in multimodal problems. Multiple subswarms are grown from an initial particleswarmby monitoring the fitness of individual

Swarmintelligence
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These proceedings contain the papers presented at ANTS 2016, the 10th International Conference onSwarmIntelligence, held at IRIDIA, UniversitLibre de Bruxelles, Brussels, Belgium, during September 7 9, 2016. The ANTS series started in 1998 with the First

A multi-objective algorithm based upon particleswarmoptimisation, an efficient data structure and turbulence
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Algorithm (MOA) based upon the ParticleSwarmOptimisation (PSO) heuristic. It utilises an order put upon members of non-dominated sets by the recent dominated tree data structure to facilitate the choosing of abestglobal individual for each member of theswarm in order to

Particleswarmoptimization
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SOCIETY. To become official, the name needs to be approved by the Board of Directors which is expected to meet on June 20, 2004. As many of you may remember, the motion to change NNSs name originated with the unanimous resolution adopted by the ADCOM at its

Application of particleswarmto multiobjective optimization
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Abstract-Evolutionary algorithms (EAs), search procedures based on natural selection (Back 1996), have been used to solve a wide variety of single and multiple objective optimization problems (Goldberg 1989). ParticleSwarmOptimization (PSO)(Kennedy and Eberhart

Particleswarmoptimization: technique, system and challenges
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ABSTRACT ParticleSwarmOptimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been

Swarmintelligence: foundations, perspectives and applications
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This chapter introduces some of the theoretical foundations ofswarmintelligence. We focus on the design and implementation of the ParticleSwarmOptimization (PSO) and Ant Colony Optimization (ACO) algorithms for various types of function optimization problems, real world

Particleswarmoptimizer in noisy and continuously changing environment
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ABSTRACT In this paper we study the performance of the recently proposed ParticleSwarm optimization method in the presence of noisy and continuously changing environments. Experimental results for well known and widely used optimization test functions are given

Stretching technique for obtaining global minimizers through particleswarmoptimization
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Abstract The ParticleSwarmOptimizer, like many other evolutionary and classical minimization methods, suffers the problem of occasional convergence to local minima, especially in multimodal and scattered landscapes. In this work we propose a modification of

A parallel particleswarmoptimization algorithm with communication strategies
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Particle swari optimization (PSO) is an alternative population-bascd cvolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problcms in continuous or binary space. We present here a parallel version of the particle

Population topologies and their influence in particleswarmperformance
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Abstract ParticleSwarmOptimization (PSO) is a new paradigm ofSwarmIntelligence. Particle swarms are a valuable tool to find optima in a fitness landscape in n, especially useful when dealing with a high number of dimensions and problems where problem

Swarmintelligence
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Difficult to predict collective behavior from individual rules.Interrogate one of the participants, it wont tell you anything about the function of the group.Small changes in rules lead to different group-level behavior.Individual behavior looks like noise: how do you

Comparative analysis of ant colony and particleswarmoptimization techniques
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ABSTRACT For a decadeswarmIntelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a

Face recognition using particleswarmoptimization-based selected features
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Abstract Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of

Initializing the particleswarmoptimizer using the nonlinear simplex method
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Abstract:-Initialization of the population in Evolutionary Computation algorithms is an issue of ongoing research. Proper initialization may help the algorithm to explore the search space more efficiently and detect better solutions. In this paper, the Nonlinear Simplex Method is

Scheduling electric power generators using particleswarmoptimization combined with the Lagrangian relaxation method
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This paper describes a procedure that uses particleswarmoptimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining

Particleswarmoptimization in dynamic environments
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Summary. This chapter reviews the application of particle swarms to dynamic optimization and explains why the canonical particleswarmoptimization (PSO) algorithm must be modified for good performance in environments such as the moving peaks benchmark. The

An analysis of publications on particleswarmoptimization applications
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Abstract Particleswarmoptimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to

Multilayer Ensemble Pruning via Novel Multi-sub- swarmParticleSwarmOptimization.
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Abstract: Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers

Swarmintelligence in data mining
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Summary This chapter presents the biological motivation and some of the theoretical concepts ofswarmintelligence with an emphasis on particleswarmoptimization and ant colony optimization algorithms. The basic data mining terminologies are explained and

Good parameters for particleswarmoptimization
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Abstract The general purpose optimization method known as ParticleSwarmOptimization (PSO) has a number of parameters that determine its behaviour and efficacy in optimizing a given problem. This paper gives a list of good choices of parameters for various optimization

A binary particleswarmoptimization algorithm for lot sizing problem
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Abstract. This paper presents a binary particleswarmoptimization algorithm for the lot sizing problem. The problem is to find order quantities which will minimize the total ordering and holding costs of ordering decisions. Test problems are constructed randomly, and solved

Special issue onswarmrobotics
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Swarmrobotics is a new approach to the coordination of multi-robot systems. In contrast with traditional multi-robot systems which use centralised or hierarchical control and communication systems in order to coordinate robots behaviours,swarmrobotics adopts a

An introduction to particleswarmoptimization
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When the search space is too large to search exhaustively, population based searches may be a good alternative, however, population based search techniques cannot guarantee you the optimal (best) solution. I will discuss a population based search technique, ParticleSwarmOptimization

Particleswarmoptimization
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Recently, a new evolutionary computation technique, known as particleswarmoptimization (PSO), has become a candidate for many optimization applications due to its high- performance and flexibility. The PSO technique was developed based on the social

Design of non uniform circular antenna arrays using particleswarmoptimization
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In this paper, the design of non-uniform circular antenna arrays with optimum side lobe level reduction is presented. The particleswarmoptimization (PSO) method, which represents a new approach for optimization problems in electromagnetics, is used in the optimization

Swarmintelligence for routing in mobile ad hoc networks.
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ABSTRACT Mobile Ad Hoc Networks are communication networks built up of a collection of mobile devices which can communicate through wireless connections. Routing is the task of directing data packets from a source node to a given destination. This task is particulary hard

Characteristics of proteoglycans extracted from theSwarmrat chondrosarcoma with associative solvents
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Proteoglycans were extracted fromSwarmrat chondrosarcoma tissue with solutions of 0.5 M guanidine hydrochloride containing protease inhibitors at pH 5.8 and 7.4. In this associative solvent, the noncovalent interactions between the components of the proteoglycan

Swarmintelligent systems
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Swarmintelligence is an innovative computational way to solving hard problems. This discipline is inspired by the behavior of social insects such as fish schools and bird flocks and colonies of ants, termites, bees and wasps. In general, this is done by mimicking the

Swarmintelligence: Literature overview
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A long time ago, people discovered the variety of the interesting insect or animal behaviors in the nature. A flock of birds sweeps across the sky. A group of ants forages for food. A school of fish swims, turns, flees together, etc. 1]. We call this kind of aggregate

Setting up PID DC motor speed control alteration parameters using particleswarmoptimization strategy
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B Allaoua, B Gasbaoui, B Mebarki Leonardo Electronic Journal of2009 193.226.7.140 Abstract In this paper, an intelligent controller of DC Motor drive is designed using particle swarmoptimization (PSO) method for formative the optimal proportional-integral-derivative (PID) controller Tuning parameters. The proposed approach has superior feature, including

Particleswarmoptimization: An introduction and its recent developments
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SwarmIntelligence Background; origin; links to EC. Introduction to PSO Original PSO, Inertia weight, constriction coefficient Particle Trajectories Simplified PSO; one or two particles Convergence aspects FIPS, Bare-bones, and other PSO variants

Optimal placement of distributed generation using particleswarmoptimization
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ABSTRACT This paper proposes a particleswarmoptimization (PSO) algorithm for optimal placement of distributed generation (DG) in a primary distribution system to minimize the total real power loss. The PSO provides a population-based search procedure in which

Effects of insulin and multiplication-stimulating activity on proteoglycan biosynthesis in chondrocytes from theSwarmrat chondrosarcoma.
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Somatomedins have been consistently reported to be more potent than insulin in stimulating proteoglycan synthesis and other anabolic responses in cartilage tissue, and pharmacologic levels of insulin have been required for significant effect. Multiplication-stimulat-ing activity

Comparison of genetic algorithm and particleswarmoptimization
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Abstract: In recent years the area of Evolutionary Computation has come into its own. Two of the popular developed approaches are Genetic Algorithms and ParticleSwarm Optimisation, both of which are used in optimisation problems. Since the two approaches

The link protein in proteoglycan aggregates from theSwarmrat chondrosarcoma.
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Aggregated proteoglycans(70% aggregated), isolated from theSwarmrat chondrosarcoma by extraction with 4 M guanidinium chloride in the presence of protease inhibitors and purified by centrifugation in an associative cesium chloride gradient, were separated into the

A discrete particleswarmoptimization approach for grid job scheduling
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Abstract. Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of

Swarmmusic: improvised music with multi-swarms
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Abstract This paper outlinesSwarmMusic, an improvisational musical system based on the dynamics of insect swarms. It is proposed that the interactions amongst performers in a freely improvising ensemble can be modelled using the self-organisational abilities of

A hybrid artificial neural networks and particleswarmoptimization for function approximation
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Abstract. In this paper, the weights of the Artificial Neural Networks (ANN) are trained by ParticleSwarmOptimization (PSO). Because PSO has the probabilistic mechanism and multi-starting points, hence the PSO can avoid getting into the local optimal solutions. The

A hybrid particleswarmand ant colony optimization for design of truss structures
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Abstract This paper presents a particleswarmant colony optimization for design of truss structures. The algorithm is based on the particleswarmoptimizer with passive congregation and ant colony optimization. The particleswarmant colony optimization applies the particle

An introduction toswarmrobotics
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Swarmrobotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large

Structure of the complex between hyaluronic acid, the hyaluronic acid-binding region, and the link protein of proteoglycan aggregates from theswarmrat
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A complex consisting of the hyaluronic acid-binding region of proteoglycan molecules, the link protein, and hyaluronic acid was purified from trypsin digests of aggregates isolated from theSwarmrat chondrosarcoma. Sodium dodecyl sulfate (SDS)-polyacrylamide gel

Theoretical analysis of particleswarmtrajectories through a mechanical analogy
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Abstract: Particleswarmoptimization (PSO) is an evolutionary computational technique used for optimization. It is motivated by the social behavior of individuals in large groups in nature. In this paper we demonstrate that PSO is a particular discretization of a damped

Gait optimization on a humanoid robot using particleswarmoptimization
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AbstractThis paper describes the application of ParticleSwarmOptimization (PSO) for gait optimization on a humanoid robot. The biped gait is modeled by a number of parameterizable trajectories. To achieve omni-directional walking, different sets of gait

Particleswarmoptimization with simulated annealing for TSP
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Abstract:-Aiming at the shortcoming of basic PSO algorithm, that is, easily trapping into local minimum, we propose an advanced PSO algorithm with SA and apply this new algorithm for solving TSP problem. The core of algorithm is based on the PSO algorithm. SA method is

Multiprocessor Scheduling Using Hybrid ParticleSwarmOptimization with Dynamically Varying Inertia.
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Abstract The problem of task assignment in heterogeneous computing systems has been studied for many years with many variations. We have developed a new hybrid approximation algorithm. The proposed hybrid heuristic model involves ParticleSwarm

Image Enhancement Using ParticleSwarmOptimization.
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AbstractApplications of the ParticleSwarmOptimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear

An hybrid fuzzy variable neighborhood particleswarmoptimization algorithm for solving quadratic assignment problems.
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Abstract: Recently, ParticleSwarmOptimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) using PSO

Particleswarmoptimization: A tutorial
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ParticleSwarmOptimization (PSO) is a technique used to explore the search space of a given problem to find the settings or parameters required to maximize a particular objective. This technique, first described by James Kennedy and Russell C. Eberhart in 1995,

Training product units in feedforward neural networks using particleswarmoptimization
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ABSTRACT Product unit (PU) neural networks are powerful because of their ability to handle higher order combinations of inputs. Training of PUs by backpropagation is however difficult, because of the introduction of more local minima. This paper compares training of a product

Particleswarmoptimization algorithm for makespan and maximum lateness minimization in permutation flowshop sequencing problem
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Abstract: In this paper, a particleswarmoptimization algorithm (PSO) is presented to solve the permutaion flowshop sequencing problem (PFSP) with the objectives of minimizing makespan and maximum lateness of jobs, respectively. Simple but very efficient local search -SOFTWARE SALES SERVICE-https://www.engpaper.net--