Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

Characterization of the LP28 strain-specific exopolysaccharide biosynthetic gene cluster found in the whole circular genome of Pediococcus pentosaceus

We have previously isolated a lactic acid bacterium (LAB), Pediococcus pentosaceus LP28, from the longan fruit Euphoria longana. Since the plant-derived LAB strain produces an extracellular polysaccharide (EPS), in this study, we analyzed the chemical structure and the biosynthesizing genes for the EPS.The EPS, which was purified from the LP28 culture broth, was classified into acidic and neutral EPSs with a molecular mass of about 50 kDa and 40 kDa, respectively. The acidic EPS consisted of glucose, galactose, mannose, and N-acetylglucosamine moieties. Interestingly, since pyruvate residue was detected in the hydrolyzed acidic EPS, one of the four sugars may be modified with pyruvate. On the other hand, the neutral EPS consisted of glucose, mannose, and N-acetylglucosamine; pyruvate was scarcely detected in the polysaccharide molecule.As a first step to deduce the probiotic function of the EPS together with the biosynthesis, we determined the whole genome sequence of the LP28 strain, demonstrating that the genome is a circular DNA, which is composed of 1,774,865 bp (1683 ORFs) with a GC content of 37.1%. We also found that the LP28 strain harbors a plasmid carrying 6 ORFs composed of 5366 bp with a GC content of 36.5%. By comparing all of the genome sequences among the LP28 strain and four strains of P. pentosaceus reported previously, we found that 53 proteins in the LP28 strain display a similarity of less than 50% with those in the four P. pentosaceus strains. Significantly, 4 of the 53 proteins, which may be enzymes necessary for the EPS production on the LP28 strain, were absent in the other four P. pentosaceus strains and displayed less than 50% similarity with other LAB species. The EPS-biosynthetic gene cluster detected only in the LP28 genome consisted of 12 ORFs containing a priming enzyme, five glycosyltransferases, and a putative polysaccharide pyruvyltransferase.     

Identification of lysophosphatidylthreonine with an aromatic fatty acid surrogate as a potent inducer of mast cell degranulation

Upon various stimulations, mast cells (MCs) release a wide variety of chemical mediators stored in their cytoplasmic granules, which then initiates subsequent allergic reactions. Lysophosphatidylserine (LysoPS), a kind of lysophospholipid, potentiates the histamine release from MCs triggered by antigen stimulation. We previously showed through structure-activity studies of LysoPS analogs that LysoPS with a methyl group at the carbon of the serine residue, i.e., lysophosphatidylthreonine (LysoPT), is extremely potent in stimulating the MC degranulation. In this study, as our continuing study to identify more potent LysoPS analogs, we developed LysoPS analogs with fatty acid surrogates. We found that the substitution of oleic acid to an aromatic fatty acid surrogate (C3-pH-p-O-C11) in 2-deoxy-1-LysoPS resulted in significant increase in the ability to induce MCs degranulation compared with 2-deoxy-1-LysoPS with oleic acid. Conversion of the serine residue into the threonine residue further increased the activity of MC degranulation both in vitro and in vivo. The resulting super agonist, 2-deoxy-LysoPT with C3-pH-p-O-C11, will be a useful tool to elucidate the mechanisms of stimulatory effect of LysoPS on MC degranulation.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.