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18
Social Programming using Functional Swarm Optimization
, 2003
"... The development of mathematical neural networks was based on an analogy with biological neural networks found in nature. Recently there has been a resurgence in research and understanding in selforganizing networks that are based on other metaphors: genetics, immune systems etc. In this paper a new ..."
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The development of mathematical neural networks was based on an analogy with biological neural networks found in nature. Recently there has been a resurgence in research and understanding in selforganizing networks that are based on other metaphors: genetics, immune systems etc. In this paper a new methodology is presented for creating Complex Adaptive Functional Networks (CAFN) that are based on the Particle Swarm socialpsychological metaphor. The proposed Social Programming methodology is base on combining the Particle Swarm methodology with The Group Method of Data Handling and Cartesian Programming.
Financial Modelling using Social Programming
, 2003
"... This paper introduces Social Programming for use in predicting closing stock prices. Social Programming is a new methodology for creating Complex Adaptive Functional Networks that is based on a socialpsychological metaphor. Social Programming is demonstrated to be a logical extension of the Particl ..."
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Cited by 5 (2 self)
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This paper introduces Social Programming for use in predicting closing stock prices. Social Programming is a new methodology for creating Complex Adaptive Functional Networks that is based on a socialpsychological metaphor. Social Programming is demonstrated to be a logical extension of the Particle Swarm methodology, the Group Method of Data Handling and Cartesian Programming. The Social Programming algorithm was able to predict closing stock prices more effectively than the traditional Group Method of Data Handling. The results in this paper illustrate the potential of the Social Programming methodology for use in financial modelling.
HW/SW Partitioning using Discrete Particle Swarm
 in Proc. ACM Great Lakes Symp. on VLSI, StresaLago Maggiore
, 2007
"... Hardware/Software partitioning is one of the most important issues of codesign of embedded systems, since the costs and delays of the final results of a design will strongly depend on partitioning. We present an algorithm based on Particle Swarm Optimization to perform the hardware/software partitio ..."
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Hardware/Software partitioning is one of the most important issues of codesign of embedded systems, since the costs and delays of the final results of a design will strongly depend on partitioning. We present an algorithm based on Particle Swarm Optimization to perform the hardware/software partitioning of a given task graph for minimum cost subject to timing constraint. By novel evolving strategy, we enhance the efficiency and result’s quality of our partitioning algorithm in an acceptable runtime. Also, we compare our results with those of Genetic Algorithm on different task graphs. Experimental results show the algorithm’s effectiveness in achieving the optimal solution of the HW/SW partitioning problem even in large task graphs.
Dynamic Clustering using Support Vector Learning with Particle Swarm Optimization
, 2005
"... Institute of Information Management IShou University This thesis presents a new approach to the support vector learning for dynamic clustering based on particle swarm optimization. Support vector clustering requires solving a constrained quadratic optimization problem. This problem often involves a ..."
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Institute of Information Management IShou University This thesis presents a new approach to the support vector learning for dynamic clustering based on particle swarm optimization. Support vector clustering requires solving a constrained quadratic optimization problem. This problem often involves a matrix with an extremely large number of entries, which make offtheshelf optimization packages unsuitable. Several methods have been used to decompose the problem, of which many require numeric packages for solving the smaller subproblems. Support vector clustering solves the unsupervised clustering problem by searching for a minimal sphere enclosing all data images in feature space. Data points are mapped
Exploring Feasible and Infeasible Regions in the Vehicle Routing Problem with Time Windows Using a MultiObjective Particle Swarm Optimization Approach
"... Abstract This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the Vehicle Routing Problem with Time Windows (VRPTW). The proposed algorithm incorporates some principles from multiobjective optimizati ..."
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Abstract This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the Vehicle Routing Problem with Time Windows (VRPTW). The proposed algorithm incorporates some principles from multiobjective optimization to allow particles to conduct a dynamic tradeoff between objectives in order to reach feasibility. The main contribution of this paper is to demonstrate that without incorporating tailored heuristics or operators to tackle infeasibility, it is possible to evolve very poor infeasible routeplans to very good feasible ones using swarm intelligence.
Fast Multiswarm Optimization with Cauchy Mutation and Crossover operation
"... Abstract. The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this pape ..."
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Abstract. The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a multiple swarms technique(FMSO) based on fast particle swarm optimization(FPSO) algorithm is proposed by bringing crossover operation. FPSO is a global search algorithm witch can prevent PSO from trapping into local optima by introducing Cauchy mutation. Though it can get high optimizing precision, the convergence rate is not satisfied, FMSO not only can find satisfied solutions,but also speeds up the search. By proposing a new information exchanging and sharing mechanism among swarms. By comparing the results on a set of benchmark test functions, FMSO shows a competitive performance with the improved convergence speed and high optimizing precision.
PERCEPTIVE PARTICLE SWARM OPTIMISATION: AN INVESTIGATION
"... Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for realworld problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the Perceptive Partic ..."
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Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for realworld problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the Perceptive Particle Swarm Optimisation (PPSO) algorithm was proposed to mimic behaviours of social animals more closely through both social interaction and environmental interaction for applications such as robot control. In this study, we investigate the PPSO algorithm on complex function optimisation problems and its ability to cope with noisy environments. finite perception range for each individual [3]. In this study, we investigate the performance of the PPSO algorithm on complex function optimization problems and its ability to cope with noisy environment. The conventional particle swarm optimisation and its modifications including the PPSO algorithm are described in section 2. The PPSO algorithm is discussed in comparison to conventional particle swarm optimisation. In section 3, the aim of the investigation and the methodology are discussed. Section 4 describes experiments to investigate the performance of PPSO and conventional particle swarm optimisation according to the methodology. A discussion of the experimental results is provided in section 5. 1.
Probabilistically Driven Particle Swarms for Optimization of Multi Valued Discrete Problems: Design and Analysis
"... Abstract—A new particle swarm optimization (PSO) algorithm that is more effective for discrete, multivalued optimization problems is presented. The new algorithm is probabilistically driven since it uses probabilistic transition rules to move from one discrete value to another in the search for an ..."
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Abstract—A new particle swarm optimization (PSO) algorithm that is more effective for discrete, multivalued optimization problems is presented. The new algorithm is probabilistically driven since it uses probabilistic transition rules to move from one discrete value to another in the search for an optimum solution. Properties of the binary discrete particle swarms are discussed. The new algorithm for discrete multivalues is designed with the similar properties. The algorithm is tested on a suite of benchmarks and comparisons are made between the binary PSO and the new discrete PSO implemented for ternary, quaternary systems. The results show that the new algorithm’s performance is close and even slightly better than the original discrete, binary PSO designed by Kennedy and Eberhart. The algorithm can be used in any real world optimization problems, which have a discrete, bounded field. I.
Sustainable Energy Systems
, 2011
"... Dissertation submitted in partial fulfilment of the requirements for the Degree of ..."
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Dissertation submitted in partial fulfilment of the requirements for the Degree of