Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Effects of neonicotinoid pesticide exposure on bee health: Molecular, physiological and behavioural investigations

Neonicotinoid exposure has been recognised as potentially impacting upon bee health, but whether realistic exposure scenarios are driving declines in bee health is not known. This thesis contributes new insights and perspectives to this research field investigating the use of molecular, physiological and behavioural endpoints as potential ecotoxicological markers for pesticide risk assessment. The thesis presents experimental data for dietary exposures of the European honey bee, Apis mellifera, and the buff-tailed bumble bee, Bombus terrestris, to one of two neonicotinoid pesticides, imidacloprid and thiamethoxam. The first part of this thesis explores impacts of chronic dietary exposures to neonicotinoid pesticides on bee immunocompetence- the ability to mount an immune response- using an artificial challenge to invoke an immune response in adult workers. Levels of phenoloxidase, an enzyme involved in melanisation and part of the bee’s defence system, were largely constitutive and resilient to exposure in honey bees and bumble bees. In honey bees, transient transcriptional changes in antimicrobial effector genes were observed following neonicotinoid exposure, but the physiological antimicrobial response was unaffected. In bumble bees, the induced antimicrobial response was impaired following neonicotinoid exposure, but only when exposed to concentrations likely higher than realistic environmental exposure scenarios. The next phase of this thesis investigates whether transcriptional, physiological and behavioural endpoints associated with the functioning of the honey bee hypopharyngeal gland were altered by imidacloprid exposure. Imidacloprid exposure led to transcriptional changes in foraging genes (associated with the control of temporal polyethism) and major royal jelly proteins (fed to developing larvae by nurse workers) and enzymatic changes in glucose oxidase (an enzyme involved in social immunity), which I hypothesise are linked with hypopharyngeal gland development. Despite these laboratory observations, no behavioural effects were observed in a field setting, monitored using Radio Frequency Identification transponders. Lastly, using RNA-Sequencing to investigate changes across the honey bee transcriptome, this thesis identified a suite of genes that were differentially expressed in adult workers in response to immune challenge and/or dietary neonicotinoid exposure. Wounding and bacterial-like infection led to upregulation of known immune genes, including a peptidoglycan recognition protein and antimicrobial effectors. Chronic exposure to thiamethoxam and imidacloprid led to downregulation of genes associated with several metabolic pathways, such as oxidative phosphorylation, pyruvate- and purine- metabolic pathways, as well as ribosomal activity. Some of these genes identified provide candidates for further study to elucidate functional effects mechanisms and better understand health outcomes, as well as potential new biomarkers for use in pesticide risk assessment. This thesis presents novel findings and offers opportunities for future research that will be of interest to a wide audience, including risk assessors and policy makers, as well as the broader biological community, including ecotoxicologists, insect physiologists and molecular biologists.Food and Environment Research Agency (Fera

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Antibiotic Molecular Design Using Artificial Bee Colony Algorithm

Research is acutely needed to develop novel therapies to treat resistant infections. This project aims to design a drug molecule via a computer aided molecular design approach to provide lead candidates for the treatment of bacterial infections caused by Staphylococcus aureus. In a recently published WHO report, a list of bacteria which pose the greatest threat to human health was given. The purpose of this report was to identify the most important resistant bacteria at global level for which immediate treatment is required. Staphylococcus aureus, which is on this list, is a pathogen causing infections such as pneumonia and bone disorders. A methodology which determines the structures of candidate antibiotic molecules is described. The Artificial Bee Colony algorithm has been used for the first time for molecular design in this work. It is necessary to predict physical and/or biological properties of compounds in order to design them. The prediction of properties is performed using Quantitative Structure Property Relationships (QSPRs). QSPRs are equations, which are developed using reported data for properties of interest by the method of regression analysis. This work applies connectivity indices and 3D MoRSE descriptors to develop QSPRs. The properties used in this work are minimum inhibitory concentration and Log P values. 3D MoRSE descriptors have been used for the first time for molecular design in this work. The QSPRs are combined with structural feasibility and connectivity constraints to formulate an optimization problem, which is a mixed integer nonlinear program (MINLP). Because of the large number of potential chemical structures and the uncertainty in the structure-property correlations, stochastic algorithms are preferred to solve the resulting MINLP. One stochastic algorithm which has shown promise to solve these problems is the Artificial Bee Colony algorithm, which relies on principles of swarm intelligence to find near-optimal solutions efficiently. The Artificial Bee Colony algorithm described in this work is used to derive solutions which serve as lead compounds for a narrowed search for novel antibiotics. Results show that the ABC algorithm is very effective in finding near optimal solutions to the MINLP, which is a combinatorial optimization problem. Molecular structures were obtained by optimizing objective function for individual property values and simultaneously for both the properties

Flexible Job-shop Scheduling Problem with Sequencing Flexibility: Mathematical Models and Solution Algorithms

Marketing strategists usually advocate increased product variety to attend better market demand. Furthermore, companies increasingly acquire more advanced manufacturing systems to take care of the increased product mix. Manufacturing resources with different capabilities give a competitive advantage to the industry. Proper management of the current productions resources is crucial for a thriving industry. Flexible job shop scheduling problem (FJSP) is an extension of the classical Job-shop scheduling problem (JSP) where operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled FJSP with sequencing flexibility (FJSPS), is studied in this work. The extension considers precedence between the operations in the form of a directed acyclic graph instead of sequential order. In this work, a mixed integer programming (MILP) formulation is presented. A single objective formulation to minimize the weighted tardiness for the FJSP with sequencing flexibility is proposed. A different objective to minimize makespan is also considered. Due to the NP-hardness of the problem, a novel hybrid bacterial foraging optimization algorithm (HBFOA) is developed to tackle the FJSP with sequencing flexibility. It is inspired by the behaviour of the E. coli bacteria. It mimics the process to seek for food. The HBFOA is enhanced with simulated annealing (SA). The HBFOA has been packaged in the form of a decision support system (DSS). A case study of a small and medium-sized enterprise (SME) manufacturing industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments with instances provided by the literature are considered. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances. In this dissertation, another critical aspect has been studied. In the industry, skilled workers usually are able to operate a specific set of machines. Hence, managers need to decide the best operation assignments to machines and workers. However, they need also to balance the workload between workers while accomplishing the due dates. In this research, a multi-objective mathematical model that minimizes makespan, maximal worker workload and weighted tardiness is developed. This model is entitled dual-resource FJSP with sequencing flexibility (DRFJSPS). It covers both the machine assignment and also the worker selection. Due to the intractability of the DRFJSPS, an elitist non-dominated sorting genetic algorithm (NSGA-II) is developed to solve this problem efficiently. The algorithm provides a set of Pareto-optimal solutions that the decision makers can use to evaluate the trade-offs of the conflicting objectives. New instances are introduced to demonstrate the applicability of the model and algorithm. A multi-random-start local search algorithm has been developed to assess the effectiveness of the adapted NSGA-II. The comparison of the solutions demonstrates that the modified NSGA-II provides a non-dominated efficient set in a reasonable time. Finally, a situation where there are multiple process plans available for a specific job is considered. This scenario is useful to be able to react to the current status of the shop where unpredictable circumstances (machine breakdown, current product mix, due dates, demand, etc.) can be accurately tackled. The determination of the process plan also depends on its cost. For that, a balance between cost, and the accomplishment of due dates is required. A multi-objective mathematical model that minimizes makespan, total processing cost and weighted tardiness are proposed to determine the sequence and the process plan to be used. This model is entitled flexible job-shop scheduling problem with sequencing and process plan flexibility (FJSP-2F). New instances are generated to show the applicability of the model

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A comprehensive survey on cultural algorithms

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