A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users’ awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Investigating Eco-evolutionary Interactions between Hosts and Members of Their Gut Microbiota

Evolutionary and ecological interactions between hosts and their associated microbial communities, their microbiota, and between members of these communities are vital to understand. Microbial communities are widespread across diverse host taxa and hosts receive a variety of well-documented benefits from their microbial communities. Despite the importance of understanding eco-evolutionary dynamics for colonization outcomes and the benefits these communities provide to their hosts, our current knowledge in this area remains incomplete. For example, we do not know the full extent of coevolution and specific relationships between hosts and microbes, and between the microbes themselves, across host taxa. Questions remain about how host taxonomy, ecology and physiology, and other present microbes influence microbial community membership and function, host and microbe evolution, and specificity in colonization of hosts. I present several studies that aim to shed further light on these eco-evolutionary topics utilizing insect pollinators, with a particular focus on bumble bees, and their gut microbial communities

The tragedy of the common? A comparative population genomic study of two bumblebee species

Within the theoretical framework of the small population paradigm, we investigated the population genomics and parasite load of two bumblebee species across the UK and Ireland. Bombus pratorum is widespread and common throughout its range while Bombus monticola is restricted to higher altitudes and shows a more fragmented distribution. Bombus monticola showed stronger population structuring, isolation-by-distance, and a deficit of heterozygotes in the most isolated population in the south of its range (Dartmoor). Heterozygosity and inbreeding coefficients (FIS) were comparable between both species, but the proportion of polymorphic sites was much greater in B. pratorum. Notably, both species have suffered significant declines in Ne over the last 100 generations and estimates and declines for both species were of similar orders of magnitude. No pattern of increased parasite prevalence in populations of lower heterozygosity was observed. Instead, ecological and demographic factors (age, latitude, date, habitat suitability) were the main drivers of parasite prevalence. Distinct patterns of selection were observed in both species in regions involved in regulation of transcription and neurotransmission and in particular pathways targeted by neonicotinoid insecticides. Our results highlight the pressing need for monitoring to include common as well as rare species. This should not focus solely on census population counts, but include estimates of Ne. We also highlight the need for further work to establish adaptive shifts in globally important pollinator communities

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced.  The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed.  At the end of summary, the applications of the SI algorithms are presented

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. At the end of summary, the applications of the SI algorithms are presented

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

© 2018 Xiao-Hong Liu et al. Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance

Behavioral and Genetic Mechanisms of Social Evolution: Insights from Incipiently and Facultatively Social Bees

Bees are highly charismatic and ecologically valuable organisms, and most popularly represented by the honey bee. Honey bees are well known in no small part because they are eusocial: a single reproductive queen continually lays eggs, and is supported by overlapping generations of thousands of non-reproductive workers, all performing specialized tasks to feed and protect the colony. Despite the ecological edge it seems to confer, eusociality has emerged in relatively few bee lineages; most have instead either remained solitary (the ancestral state for all bees) or demonstrate any of a range of less derived forms of social organization. Researchers have for decades been steadily teasing out the ecological, developmental, and evolutionary factors that may drive the emergence and elaboration of insect social complexity. This dissertation aims to join that effort by offering a handful of additional insights emerging from empirical testing of major social evolutionary hypotheses in bees of facultative and early sociality. My introductory Chapter 1 elaborates on the question of eusociality in greater detail and lays out the major social evolutionary hypotheses and their syntheses. I argue in support of research among bees of early or facultative sociality as systems in which much-needed empirical testing of evolutionary theory may be performed. In Chapter 2, I use relatedness and demographic data to calculate the inclusive fitness costs and benefits of social nesting in the small carpenter bee (Ceratina calcarata) which may rear a single worker-like daughter to aid in brood care. I find that social nesting may be advantageous to social nest mothers rather than daughters in this species, contrary to the expectations of kin selection theory. In Chapter 3, I further investigate sociality in C. calcarata using brain transcriptomic data that captures patterns of cis-regulation and gene expression associated with female maturation and two well-defined behavioral states, foraging and guarding, concurrently demonstrated by mothers and daughters in social nests. I find that the early social nest environment may have a strong effect on gene expression; and reveal foraging and guarding behaviors to be underpinned by deeply conserved genes that are differentially expressed within a highly modular gene network. In Chapter 4, I draw on another set of brain transcriptomic data, this time reflecting first and second year solitary females, queens, and workers of the long-lived and facultatively eusocial small carpenter bee, Ceratina japonica. I find that queen and worker phenotypes are underpinned by highly divergent gene regulatory pathways. I also show how genes underlying C. japonica’s queens and workers are well-conserved and demonstrate strikingly similar patterns of expression in other bees of early eusociality. I also discover that while the social nest environment may induce some shared shifts in lifetime gene expression among queens and workers vs solitary females, the role of oxidative damage reduction may be a proximate mechanism of prolonged longevity regardless of social phenotype. Appendix A details my development of polymorphic microsatellite markers using the C. calcarata genome, which were used in Chapter 2 of this dissertation and are now a publicly available tool for further research; the remaining Appendices provide mainly supplementary methods and figures for Chapters 3 and 4