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
Papers
More filters
TL;DR: This paper describes a method of designing a reconfigurable dual‐beam antenna array using a new evolutionary algorithm called particle swarm optimization (PSO) to find element excitations that will result in a sector pattern main beam with low side lobes.
Abstract: Multiple-beam antenna arrays have important applica- tions in communications and radar. This paper describes a method of designing a reconfigurable dual-beam antenna array using a new evolu- tionary algorithm called particle swarm optimization (PSO). The design problem is to find element excitations that will result in a sector pattern main beam with low side lobes with the additional requirement that the same excitation amplitudes applied to the array with zero phase should result in a high directivity, low side lobe, and pencil-shaped main beam. Two approaches to the optimization are detailed. First, the PSO is used to optimize the coefficients of the Woodward-Lawson array synthesis method. Second, the element excitations will be optimized directly using PSO. The performance of the two methods is compared and the viability of the resulting designs are discussed in terms of sensitivity to errors in the excitation. Additionally, a parallel version of the particle swarm code developed for a multi-node Beowulf cluster and the benefits that multi- node computing bring to global optimization will be discussed. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 168-175, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.11005
238 citations
TL;DR: The algorithm is compared with other state-of-the-art SA algorithms and advanced global optimization methods and found better designs than the other SA-based algorithms and converged much more quickly to the optimum than HPSO and HS.
238 citations
TL;DR: A meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique, which demonstrates high computational efficiency in constructing optimal risky portfolios.
Abstract: One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.
238 citations
TL;DR: This paper argues in favor of modifier adaptation, since it uses a model parameterization and an update criterion that are well tailored to meeting the KKT conditions of optimality.
238 citations
TL;DR: In this article, the authors explored the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point.
238 citations