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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 bee swarm for numerical optimization
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Swarm intelligence has become a research interest to many research scientists of related fields in recent years. Bonabeau has defined the swarm intelligence as any attempt to design algorithms or distributed problem-solving devices inspired by the collective

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

The swarm simulation system: A toolkit for building multi-agent simulations
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Abstract Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of simulation is the swarm a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches

Multi-objective particle swarm optimizers: A survey of the state-of-the-art
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Abstract: The success of the Particle Swarm Optimization (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 particle swarm optimizer-the ARPSO
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Abstract The particle swarm optimization (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

Swarm intelligence in optimization
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Summary. Optimization techniques inspired by swarm intelligence 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

Particle swarm optimization
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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

Swarm intelligence: 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 particle swarm optimization to dynamic environments
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Abstract In this paper the authors propose a method for adapting the particle swarm 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 particle swarm optimizer
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ABSTRACT This paper describes a technique that extends the unimodal particle swarm optimizer to efficiently locate multiple optimal solutions in multimodal problems. Multiple subswarms are grown from an initial particle swarm by monitoring the fitness of individual

Swarm intelligence
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These proceedings contain the papers presented at ANTS 2016, the 10th International Conference on Swarm Intelligence, held at IRIDIA, Universit Libre de Bruxelles, Brussels, Belgium, during September 7 9, 2016. The ANTS series started in 1998 with the First

A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence
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Algorithm (MOA) based upon the Particle Swarm Optimisation (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 the swarm in order to

Particle swarm optimization
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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 particle swarm to 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). Particle Swarm Optimization (PSO)(Kennedy and Eberhart

Particle swarm optimization: technique, system and challenges
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ABSTRACT Particle Swarm Optimization (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

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

Particle swarm optimizer in noisy and continuously changing environment
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ABSTRACT In this paper we study the performance of the recently proposed Particle Swarm 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 particle swarm optimization
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Abstract The Particle Swarm Optimizer, 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 particle swarm optimization 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 particle swarm performance
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Abstract Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence. 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

Swarm intelligence
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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 particle swarm optimization techniques
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ABSTRACT For a decade swarm Intelligence, 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 particle swarm optimization-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 particle swarm optimizer 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 particle swarm optimization combined with the Lagrangian relaxation method
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This paper describes a procedure that uses particle swarm optimization (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

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

An analysis of publications on particle swarm optimization applications
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Abstract Particle swarm optimisation (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- swarm Particle Swarm Optimization.
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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

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

Good parameters for particle swarm optimization
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Abstract The general purpose optimization method known as Particle Swarm Optimization (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 particle swarm optimization algorithm for lot sizing problem
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Abstract. This paper presents a binary particle swarm optimization 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 on swarm robotics
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Swarm robotics 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, swarm robotics adopts a

An introduction to particle swarm optimization
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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, Particle Swarm Optimization

Particle swarm optimization
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Recently, a new evolutionary computation technique, known as particle swarm optimization (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 particle swarm optimization
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In this paper, the design of non-uniform circular antenna arrays with optimum side lobe level reduction is presented. The particle swarm optimization (PSO) method, which represents a new approach for optimization problems in electromagnetics, is used in the optimization

Swarm intelligence 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 the Swarm rat chondrosarcoma with associative solvents
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Proteoglycans were extracted from Swarm rat 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

Swarm intelligent systems
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Swarm intelligence 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

Swarm intelligence: 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 particle swarm optimization strategy
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B Allaoua, B Gasbaoui, B Mebarki Leonardo Electronic Journal of 2009 193.226.7.140 Abstract In this paper, an intelligent controller of DC Motor drive is designed using particle swarm optimization (PSO) method for formative the optimal proportional-integral-derivative (PID) controller Tuning parameters. The proposed approach has superior feature, including

Particle swarm optimization: An introduction and its recent developments
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Swarm Intelligence 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 particle swarm optimization
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ABSTRACT This paper proposes a particle swarm optimization (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 the Swarm rat 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 particle swarm optimization
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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 Particle Swarm Optimisation, both of which are used in optimisation problems. Since the two approaches

The link protein in proteoglycan aggregates from the Swarm rat chondrosarcoma.
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Aggregated proteoglycans(70% aggregated), isolated from the Swarm rat 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 particle swarm optimization 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

Swarm music: improvised music with multi-swarms
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Abstract This paper outlines Swarm Music, 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 particle swarm optimization for function approximation
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Abstract. In this paper, the weights of the Artificial Neural Networks (ANN) are trained by Particle Swarm Optimization (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 particle swarm and ant colony optimization for design of truss structures
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Abstract This paper presents a particle swarm ant colony optimization for design of truss structures. The algorithm is based on the particle swarm optimizer with passive congregation and ant colony optimization. The particle swarm ant colony optimization applies the particle

An introduction to swarm robotics
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Swarm robotics 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 the swarm rat
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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 the Swarm rat chondrosarcoma. Sodium dodecyl sulfate (SDS)-polyacrylamide gel

Theoretical analysis of particle swarm trajectories through a mechanical analogy
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Abstract: Particle swarm optimization (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 particle swarm optimization
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Abstract This paper describes the application of Particle Swarm Optimization (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

Particle swarm optimization 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 Particle Swarm Optimization 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 Particle Swarm

Image Enhancement Using Particle Swarm Optimization.
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Abstract Applications of the Particle Swarm Optimization (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 particle swarm optimization algorithm for solving quadratic assignment problems.
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Abstract: Recently, Particle Swarm Optimization (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

Particle swarm optimization: A tutorial
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Particle Swarm Optimization (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 particle swarm optimization
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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

Particle swarm optimization algorithm for makespan and maximum lateness minimization in permutation flowshop sequencing problem
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Abstract: In this paper, a particle swarm optimization 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
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