Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Speed limitation of a mobile robot and methodology of tracing odor plume in airflow environments

AbstractThe methodology of tracing odor plume via a mobile robot is considered. In this research, two typical plume-tracing methods, i.e., a zigzagging method and an upwind method, are tested in four airflow fields with different long-time average wind speeds when the robot is set to possessing four different maximum speeds. According to the simulation results, it can be deduced that the zigzagging algorithms would be efficient when the robot moves faster than the odor plume or airflow, and the upwind algorithms are preferred especially when the robot is slow

Development of an indirect enzyme-linked immunosorbent assay (ELISA) assay based on a recombinant truncated VP2 (tVP2) protein for the detection of canine parvovirus antibodies

By removing the N-terminal hydrophobic sequence, truncated VP2 (tVP2) genes were cloned into the pET-32a (+) plasmid and subsequently expressed as His fusion proteins. The purified recombinant tVP2 proteins were specific to canine parvovirus (CPV), and one of them was used in an indirect enzyme-linked immunosorbent assay (ELISA) for the detection of CPV antibodies. The minimum detection limit of this method was 1:1280. There was good agreement between tVP2-based indirect ELISA and the commercially available diagnostic kit. The results suggest that the recombinant tVP2 protein-based ELISA could be used to detect CPV antibodies.Key words: Canine parvovirus, recombinant truncated VP2 (tVP2), enzyme-linked immunosorbent assay (ELISA), antibody detection