A meta-heuristic optimization approach for optimizing cross-pollination using UAVs

ABSTRACT Pollination using Unmanned Aerial Vehicles (UAVs) has emerged as a promising solution to the current pollination crisis. The dwindling number of natural pollinators forces the production of cutting-edge pollination technologies. This work proposes a module to optimize path planning for UAVs to travel in a minimum time. This study suggests a novel approach to maximize cross-pollination and minimize travel time with a highly efficient meta-heuristic optimization algorithm. This paper briefly describes a process we previously developed for flower insights that includes flower gender and gene identification and classification. With an insight into flowers, the proposed algorithm aims to achieve efficient and accurate pollination while minimizing energy consumption and convergence time. The Versatile Flower Pollination Algorithm’s (VFPA) approach is superior because it significantly reduces the amount of computing required while maintaining almost optimal performance. The proposed algorithm was successfully implemented to compute the distance between the male and female flowers and transfer nectar with a difference in the nectar value. The proposed approach shows promise for addressing the pollination crisis and reducing the reliance on traditional methods

Breaking the spell of nestedness: The entropic origin of nestedness in mutualistic systems

Mutualistic interactions, those that are naturally beneficial for both interacting species, are recurrently found in ecosystems. Observations of natural systems show that if we draw mutualistic relationships as links between species, the resulting mutualistic network of interactions displays a widespread particular ordering called nestedness. In such an ordering, the mutualistic partners of a given species conform a subset of the partners of all species with larger degree, that is, of those species having more interactions. On the other hand, theoretical works show that a nested structure has a positive impact on a number of relevant features of mutualistic communities ranging from species coexistence to structural stability and biodiversity. However, how nestedness emerges and what are its determinants, are still open challenges that have led to multiple debates to date. Here we show, by applying a theoretical approach to the analysis of 167 real mutualistic networks, that nestedness is not an irreducibly macroscopic feature but an entropic consequence of the degree sequences (number of mutualistic interactions of each species). Remarkably, we find that an outstanding majority of the analyzed networks does not show statistically significant nestedness. These findings point to the need of revising previous claims about the role of nestedness and might contribute to expand our understanding of how evolution shapes mutualistic interactions and communities by placing the focus on the node-dependent properties rather than on global quantities

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance

Evolution and loss of long-fringed petals

Background: The Cucurbitaceae genus Trichosanthes comprises 90–100 species that occur from India to Japan and southeast to Australia and Fiji. Most species have large white or pale yellow petals with conspicuously fringed margins, the fringes sometimes several cm long. Pollination is usually by hawkmoths. Previous molecular data for a small number of species suggested that a monophyletic Trichosanthes might include the Asian genera Gymnopetalum (four species, lacking long petal fringes) and Hodgsonia (two species with petals fringed). Here we test these groups’ relationships using a species sampling of c. 60% and 4759 nucleotides of nuclear and plastid DNA. To infer the time and direction of the geographic expansion of the Trichosanthes clade we employ molecular clock dating and statistical biogeographic reconstruction, and we also address the gain or loss of petal fringes. Results: Trichosanthes is monophyletic as long as it includes Gymnopetalum, which itself is polyphyletic. The closest relative of Trichosanthes appears to be the sponge gourds, Luffa, while Hodgsonia is more distantly related. Of six morphology-based sections in Trichosanthes with more than one species, three are supported by the molecular results; two new sections appear warranted. Molecular dating and biogeographic analyses suggest an Oligocene origin of Trichosanthes in Eurasia or East Asia, followed by diversification and spread throughout the Malesian biogeographic region and into the Australian continent. Conclusions: Long-fringed corollas evolved independently in Hodgsonia and Trichosanthes, followed by two losses in the latter coincident with shifts to other pollinators but not with long-distance ispersal events. Together with the Caribbean Linnaeosicyos, the Madagascan Ampelosicyos and the tropical African Telfairia, these cucurbit lineages represent an ideal system for more detailed studies of the evolution and function of petal fringes in plant-pollinator mutualisms

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area