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.
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01 Jan 2015
TL;DR: The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Abstract: Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these
algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms.
Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered
approaches.
117 citations
TL;DR: A comprehensive review of population topologies developed for PSO and DE is carried out and it is anticipated that this survey will inspire researchers to integrate the populationTopologies into other nature inspired algorithms and to develop novel population topology for improving the performances of population-based optimization algorithms for solving single objective optimization, multiobjective optimization and other classes of optimization problems.
Abstract: Over the last few decades, many population-based swarm and evolutionary algorithms were introduced in the literature. It is well known that population topology or sociometry plays an important role in improving the performance of population-based optimization algorithms by enhancing population diversity when solving multiobjective and multimodal problems. Many population structures and population topologies were developed for particle swarm optimization and differential evolutionary algorithms. Therefore, a comprehensive review of population topologies developed for PSO and DE is carried out in this paper. We anticipate that this survey will inspire researchers to integrate the population topologies into other nature inspired algorithms and to develop novel population topologies for improving the performances of population-based optimization algorithms for solving single objective optimization, multiobjective optimization and other classes of optimization problems.
117 citations
TL;DR: In this article, a particle swarm optimization approach for inventory classification problems is presented, where inventory items are classified based on a specific objective or multiple objectives, such as minimizing costs, maximizing inventory turnover ratios, and maximizing inventory correlation.
117 citations
TL;DR: The experimental results show that the proposed prognostic approach has high prediction accuracy and the proposed model needs fewer parameters than the traditional empirical models.
Abstract: A novel data-driven prognostic approach for lithium-ion batteries remaining useful life (RUL) based on the Verhulst model, particle swarm optimization (PSO) and particle filter (PF) is proposed. First, the Verhulst model based on the capacity fade trends of lithium-ion batteries is proposed, which is used as the fitting model and predicting model, respectively. Second, the PSO is applied to improve the fitting model. Third, the improved fitting model combined with the Euclidean distance is employed to determine the upper and lower bounds of the predicting model parameters. Fourth, to estimate the predicting model, the PSO is exploited based on the upper and lower bounds of parameters. Then, to compensate the prediction error, the PF is used to update the predicting model. Finally, the RUL prediction can be made by extrapolating the updated predicting model to the acceptable performance threshold. Four case studies are conducted to validate the proposed approach. The experimental results show the following: 1) the proposed prognostic approach has high prediction accuracy and 2) the proposed model needs fewer parameters than the traditional empirical models.
117 citations
Journal Article•
TL;DR: In this article, a new particle swarm optimization which improves particle's velocity and position update rule to adjust its movement based on the individual best position is proposed, which can enhance capability of optimization.
Abstract: Particle swarm optimization is a new computational method for tackling optimization functionsHowever,it is easily trapped into the local optimization when solving high-dimension functionsTo overcome this shortcoming,a new particle swarm optimization which improves particle's velocity and position update rule to adjust its movement based on the individual best position is proposed in the paperThe modified algorithm can enhance capability of optimizationFive benchmark functions are tested,and the results indicate that the modified particle swarm optimization is effective to find the global optimal solution
117 citations