An Improved Brain Storm Optimization with Dynamic Clustering Strategy

Intelligence algorithms play an increasingly important role in the field of intelligent control. Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by emulating the collective behavior of human beings in the problem solving process. To improve the performance of the original BSO, many variants of BSO are proposed. In this paper, an improved BSO algorithm with dynamic clustering strategy (BSO-DCS) is proposed as a variant of BSO for global optimization problems. The basic framework of BSO is firstly introduced. Then to reduce the time complexity of the original BSO, a new grouping method named dynamic clustering strategy (DCS) is proposed to improve the clustering method in the original BSO. To verify the effectiveness of the proposed BSO-DCS, it is tested on 12 benchmark functions of CEC 2005 with 30 dimensions. Experimental results show that DCS is an effective strategy to reduce the time complexity, and the improved BSO-DCS performs greatly better than the original BSO algorithm

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

Active PSF shaping and adaptive optics enable volumetric localization microscopy through brain sections

Application of single-molecule switching nanoscopy (SMSN) beyond the coverslip surface poses substantial challenges due to sample-induced aberrations that distort and blur single-molecule emission patterns. We combined active shaping of point spread functions and efficient adaptive optics to enable robust 3D-SMSN imaging within tissues. This development allowed us to image through 30-μm-thick brain sections to visualize and reconstruct the morphology and the nanoscale details of amyloid-β filaments in a mouse model of Alzheimer's disease

A Simple Brain Storm Optimization Algorithm with a Periodic Quantum Learning Strategy

Brain storm optimization (BSO) is a young and promising population-based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies such as K-means clustering, resulting in large computational burdens. The paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering (SIC) strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating (SIU) strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduces redundancy in the pattern for generating individuals. Third, a quantum-behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum-behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, Particle Swarm Optimization (PSO), and Differential Evolution (DE) on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms

A Brain Storm Optimization with Multiinformation Interactions for Global Optimization Problems

The original BSO fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback (ISF) mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms

最適化問題に対するブレインストーム最適化アルゴリズムの改善

富山大学・富理工博甲第170号・于洋・2020/3/24富山大学202