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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a multiobjective reliability-based design optimization (MORBDO) procedure is proposed to explore the design of vehicle door, which is capable of generating a well-distributed Pareto frontier of reliable solutions, and it is suggested to select an optimum from relative insensitive regions.

101 citations

Journal ArticleDOI
TL;DR: The proposed improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve the parameter identification problem for chaotic dynamic systems and is illustrated in simulations that it is more successful than the SPSO and GA.
Abstract: This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification.

101 citations

Book ChapterDOI
11 Apr 2007
TL;DR: Using a geometric framework for the interpretation of crossover, an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms is shown that enables PSO to generalize to virtually any solution representation in a natural and straightforward way.
Abstract: Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.

101 citations

Proceedings ArticleDOI
08 Jul 2006
TL;DR: A gregarious particle swarm optimization algorithm (G-PSO) in which the particles explore the search space by aggressively scouting the local minima with the help of only social knowledge, which reduces the computation effort.
Abstract: This paper presents a gregarious particle swarm optimization algorithm (G-PSO) in which the particles explore the search space by aggressively scouting the local minima with the help of only social knowledge. To avoid premature convergence of the swarm, the particles are re-initialized with a random velocity when stuck at a local minimum. Furthermore, G-PSO adopts a "reactive" determination of the step size, based on feedback from the last iterations. This is in contrast to the basic particle swarm algorithm, in which the particles explore the search space by using both the individual "cognitive" component and the "social" knowledge and no feedback is used for the self-tuning of algorithm parameters. The novel scheme presented, besides generally improving the average optimal values found, reduces the computation effort.

101 citations

Proceedings ArticleDOI
01 Jan 2005
TL;DR: The results of a performance evaluation of four extensions of Particle Swarm Optimisation (PSO) to reduce energy consumption in wireless sensor networks are described and a distance based clustering criterion for sensor network optimisation is adopted.
Abstract: We describe the results of a performance evaluation of four extensions of Particle Swarm Optimisation (PSO) to reduce energy consumption in wireless sensor networks. Communication distances are an important factor to be reduced in sensor networks. By using clustering in a sensor network we can reduce the total communication distance, thus increasing the life of a network. We adopt a distance based clustering criterion for sensor network optimisation. From PSO perspective, we study the suitability of four different PSO algorithms for our application and propose modifications. An important modification proposed is to use a boundary checking routine for re-initialisation of a particle which moves outside the set boundary.

101 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171