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

Active Collision Avoidance for Human-Robot Interaction With UKF, Expert System, and Artificial Potential Field Method

With the development of Industry 4.0, the cooperation between robots and people is increasing. Therefore, man—machine security is the first problem that must be solved. In this paper, we proposed a novel methodology of active collision avoidance to safeguard the human who enters the robot's workspace. In the conventional approaches of obstacle avoidance, it is not easy for robots and humans to work safely in the common unstructured environment due to the lack of the intelligence. In this system, one Kinect is employed to monitor the workspace of the robot and detect anyone who enters the workspace of the robot. Once someone enters the working space, the human will be detected, and the skeleton of the human can be calculated in real time by the Kinect. The measurement errors increase over time, owing to the tracking error and the noise of the device. Therefore we use an Unscented Kalman Filter (UKF) to estimate the positions of the skeleton points. We employ an expert system to estimate the behavior of the human. Then let the robot avoid the human by taking different measures, such as stopping, bypassing the human or getting away. Finally, when the robot needs to execute bypassing the human in real time, to achieve this, we adopt a method called artificial potential field method to generate a new path for the robot. By using this active collision avoidance, the system can achieve the purpose that the robot is unable to touch on the human. This proposed system highlights the advantage that during the process, it can first detect the human, then analyze the motion of the human and finally safeguard the human. We experimentally tested the active collision avoidance system in real-world applications. The results of the test indicate that it can effectively ensure human security

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals’ creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals’ update (H-IU) scheme is established by reasonably combing two different individuals’ update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared 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

An enhanced brain storm sine cosine algorithm for global optimization problems

The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability

A Surface Mass-Spring Model with New Flexion Springs and Collision Detection Algorithms Based on Volume Structure for Real-time Soft-tissue Deformation Interaction

A critical problem associated with surgical simulation is balancing deformation accuracy with real-time performance. Although the canonical surface mass-spring model (MSM) can provide an excellent real-time performance, it fails to provide effective shape restoration behavior when generating large deformations. This significantly influences its deformation accuracy. To address this problem, this paper proposes a modified surface MSM. In the proposed MSM, a new flexion spring is first developed to oppose bending based on the included angle between the initial position vector and the deformational position vector, improving the shape restoration performance and enhance the deformational accuracy of MSM; then, a new type of surface triangular topological unit is developed for enhancing the computational efficiency and better adapting to the different topological soft tissue deformational models. In addition, to further improve the accuracy of deformational interactions between the soft tissue and surgical instruments, we also propose two new collision detection algorithms. One is the discrete collision detection with the volumetric structure (DCDVS), applying a volumetric structure to extend the effective range of collision detection; the other is the hybrid collision detection with the volumetric structure (HCDVS), introducing the interpolation techniques of the continuous collision detection to DCDVS. Experimental results show that the proposed MSM with DCDVS or HCDVS can achieve accurate and stable shape restoration and show the real-time interactive capability in the virtual artery vessel and heart compared with the canonical surface MSM and new volume MSM