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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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Proceedings ArticleDOI
25 Jun 2005
TL;DR: This work introduces the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems and proposes two new perturbation operators: "c-perturbation" and "m-perturbedation".
Abstract: We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "m-perturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules. PESO is compared with respect to a highly competitive technique representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. PESO matches most results and outperforms other PSO algorithms.

109 citations

Journal ArticleDOI
TL;DR: An enhanced multistep strategy based on a multiresolution particle swarm optimizer is proposed for 3-D microwave imaging to improve the convergence capabilities and to reduce the dimension of the search space and the computational burden of the optimization strategy, thanks to a constrained control of the particle velocities adaptively determined.
Abstract: An enhanced multistep strategy based on a multiresolution particle swarm optimizer is proposed for 3-D microwave imaging. The aim of such an integration is to improve the convergence capabilities of the approach and to reduce the dimension of the search space and the computational burden of the optimization strategy, thanks to a constrained control of the particle velocities adaptively determined. This favors the exploitation of the global search capabilities of the particle swarms also in the framework of large-scale 3-D inverse scattering problems. The proposed technique is assessed by considering numerical tests concerned with single and multiple 3-D targets. The results of an experimental testing are also discussed.

109 citations

Journal ArticleDOI
TL;DR: A new soft computing method called the parameter-free simplified swarm optimization (SSO)-based artificial neural network (ANN), or improved SSO for short, is proposed to adjust the weights in ANNs.
Abstract: A new soft computing method called the parameter-free simplified swarm optimization (SSO)-based artificial neural network (ANN), or improved SSO for short, is proposed to adjust the weights in ANNs. The method is a modification of the SSO, and seeks to overcome some of the drawbacks of SSO. In the experiments, the iSSO is compared with five other famous soft computing methods, including the backpropagation algorithm, the genetic algorithm, the particle swarm optimization (PSO) algorithm, cooperative random learning PSO, and the SSO, and its performance is tested on five famous time-series benchmark data to adjust the weights of two ANN models (multilayer perceptron and single multiplicative neuron model). The experimental results demonstrate that iSSO is robust and more efficient than the other five algorithms.

109 citations

Journal ArticleDOI
TL;DR: An effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules.
Abstract: In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.

108 citations

Journal ArticleDOI
TL;DR: In this paper, a modified quantum-behaved particle swarm optimization (QPSO) was proposed for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints.

108 citations


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