Topic
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 published on a yearly basis
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TL;DR: A new optimization algorithm called multifrequency vibrational PSO is significantly improved and tested for two different test cases: optimization of six different benchmark test functions and direct shape optimization of an airfoil in transonic flow.
Abstract: Particle swarm optimization (PSO), a relatively new population-based intelligence algorithm, exhibits good performance on optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged most likely around the local solution. A new optimization algorithm called multifrequency vibrational PSO is significantly improved and tested for two different test cases: optimization of six different benchmark test functions and direct shape optimization of an airfoil in transonic flow. The algorithm emphasizes a new mutation application strategy and diversity variety, such as global random diversity and local controlled diversity. The results offer insight into how the mutation operator affects the nature of the diversity and objective function value. The local controlled diversity is based on an artificial neural network. As far as both the demonstration cases' problems are considered, remarkable reductions in the computational times have been accomplished.
115 citations
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04 Dec 2009TL;DR: A new CSO algorithm for clustering problem and the comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method.
Abstract: Cat Swarm Optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle Swarm Optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed. The new CSO clustering algorithm was tested on four different datasets. The modification is made on the CSO formula to obtain better results. Then, the accuracy level of poposed algorith was compared to those of K-means and PSO clustering. The modification of CSO formula can improve the performance of CSO Clustering. The comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method
115 citations
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TL;DR: The approach takes advantage of the PSO ability to find global optimum in problems with complex design spaces while directly enforcing feasibility of constraints using an augmented Lagrange multiplier method to find better solutions for structural optimization tasks.
115 citations
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TL;DR: Experimental results demonstrate that both CGSA1 and CGSA2 perform better than GSA and other five chaotic particle swarm optimization problems.
115 citations
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08 Sep 2010TL;DR: A heterogeneous PSO (HPSO) is proposed, where particles are allowed to follow different search behaviours selected from a behaviour pool, thereby efficiently addressing the exploration-exploitation trade-off problem.
Abstract: Particles in the standard particle swarm optimization (PSO) algorithms, and most of its modifications, follow the same behaviours. That is, particles implement the same velocity and position update rules. This means that particles exhibit the same search characteristics. A heterogeneous PSO (HPSO) is proposed in this paper, where particles are allowed to follow different search behaviours selected from a behaviour pool, thereby efficiently addressing the exploration-exploitation trade-off problem. A preliminary empirical analysis is provided to show that much can be gained by using heterogeneous swarms.
115 citations