Particle swarm optimization - mass-spring system analogon

Brandstätter, Bernhard; Baumgartner, Ulrike

doi:10.1109/20.996256

Cited by 107 publications

(54 citation statements)

References 3 publications

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“…Inspired by social behavior of bird flocking or fish schooling, a number of scientists have created computer simulations of various interpretations of the movement of organisms in a bird flock or fish school [24,25]. These simulations relied heavily on manipulation of inter-individual distances; that is, the synchrony of flocking behavior was thought to be a function of birds' efforts to maintain an optimum distance between themselves and their neighbors.…”

Section: Basic Pso Algorithmsmentioning

confidence: 99%

Particle Swarm Optimization for Correlative product combinatorial introduction model

Wan

Dong

2009

2009 Chinese Control and Decision Conference

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Enterprises need to develop a process to determine how to find and develop new product ideas and finally, how to successfully introduce them to the marketplace. To address this problem, conceptions of Optimal Introduction Period and Correlative Profit were presented. Based on the quantitative description of the product life cycle, a Non-Linear Semi-Infinite Programming model of new product introduction was proposed. The proposed model was solved by improved Particle Swarm Optimization (PSO) algorithms. Optimal solution of the given example shows that Particle Swarm Optimization has become the hotspot of evolutionary computation because of its excellent performance and simplicity for implement in solving combined optimization problems.

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Section: Basic Pso Algorithmsmentioning

confidence: 99%

Particle Swarm Optimization for Correlative product combinatorial introduction model

Wan

Dong

2009

2009 Chinese Control and Decision Conference

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“…PSO has been applied to engineering and non-engineering problems proving its ability to find optimal solution with low computational cost [60][61][62][63][64][65]. In addition, research has also been done to compare PSO with other evolutionary algorithms [66][67][68] to assess the effectiveness of the technique.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Advanced Polymer Composite Molding Through Intelligent Process Analysis and Control

Minaie¹

2007

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“…Particle swarm optimization is a population based optimization technique with the inspiration from the social behavior of a swarm of birds (particles) that "fly" through a solution space [1,13]. Each particle accomplishes its own updating based on its current velocity and position, the best position seen so far by itself and by the swarm.…”

Section: Particle Swarm Optimization In Dynamic Environmentsmentioning

confidence: 99%

“…The height and width of each peak were randomly generated with a uniform distribution in [30,70] and [1,12] respectively. The locations of peaks are changed as follows:…”

Section: Experimental Studymentioning

confidence: 99%

Compound Particle Swarm Optimization in Dynamic Environments

Liu

Wang

Yang

2008

Lecture Notes in Computer Science

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Abstract. Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of particles in the search space and their motions are integrated into the swarm. A special reflection scheme is introduced in order to explore the search space more comprehensively. Furthermore, some information preserving and anti-convergence strategies are also developed to improve the performance of CPSO in a new environment. An experimental study shows the efficiency of CPSO in dynamic environments.

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Particle swarm optimization - mass-spring system analogon

Cited by 107 publications

References 3 publications

Particle Swarm Optimization for Correlative product combinatorial introduction model

Particle Swarm Optimization for Correlative product combinatorial introduction model

Advanced Polymer Composite Molding Through Intelligent Process Analysis and Control

Compound Particle Swarm Optimization in Dynamic Environments

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