QoS-driven metaheuristic service composition schemes: a comprehensive overview

Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem. As a result, numerous composition schemes have been suggested in the literature to untangle this problem by using various metaheuristic algorithms. This paper presents a comprehensive survey and taxonomy of such QoS-oriented metaheuristic WS composition schemes provided in the literature. It investigates how metaheuristic algorithms are adapted for the WS composition problem and highlight their main features, advantages, and limitations. Also, in each category of the studied composition schemes, a comparison of their applied QoS factors, evaluated metrics, exploited simulators, and properties of the applied metaheuristic algorithms are explained. Finally, the concluding remarks and future research directions are summarized to help researchers in working in this area.

     

DrawBot: a bio-inspired robotic portraitist

Abstract

We are developing the control architecture of a portraitist artificial agent called DrawBot that reproduces the visuomotor behaviour of a human carrying out a realistic portrait. The visuomotor strategy adopted by DrawBot is based on computational models of eye movements in human beings,and one xperimental findings on eye-hand coordination in expert draughtsmen. In this paper we present a behavioural model of the visuomotor coordination adopted by a draughtsman, designed in terms of visual routines.Eventually we outline the implementation of the basic routines.     

A bio-inspired emergent system for intelligent Web service composition and management

Some important mechanisms in neuroendocrine-immune (NEI) system are inspired to design a decentralized, evolutionary, scalable, and adaptive system for Web service composition and management. We first abstract a novel intelligent network model inspired from the NEI system. Based on this model, we then propose a method for Web service emergence by designing a bio-entity as an autonomous agent to represent Web service. As such, automatic composition and dynamic management of Web services can be achieved. Also, we build its computation platform which allows the bio-entities to cooperate over Web services and exploits capabilities of their partners. Finally, the simulation results on the platform show that Web service emergence can be achieved through self-organizing, cooperating, and compositing. The proposed method provides a novel solution for intelligent composition and management of Web services.     

Correction to: QoS-driven metaheuristic service composition schemes: a comprehensive overview

Artificial Intelligence Review - In the original publication of the article, the corresponding author name and the e-mail ID was incorrect. The correct name and e-mail ID is given in this correction.     

A new design methodology of electrostrictive actuators for bio-inspired robotics

At present advanced robotics concepts require new and more suitable components to be exploited, especially in the fields of the biomimetic and soft robotics. In this sense, the actuation system represents one of the most limiting factors for the realization of robots with bio-inspired features and performances.This paper presents a new soft actuators based on electroactive polymers (EAPs) technology. The actuator is composed of a pre-stretched silicone film sputtered with a very thin gold film on both sides, working as electrodes. A particular folded geometry, implemented through an innovative fabrication process, allows to exploit the electrostrictive effect and to develop soft actuators suitable in many applications where softness and flexibility are necessary. The manufactured prototypes were developed on the basis of a parametric model that takes into account all geometric parameters and material characteristics. The proposed model is useful to estimate the performances of the actuator and to improve them.     

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules.     

BERT and Pareto dominance applied to biological strategy decision for bio-inspired design

Bio-inspired design (BID) approaches have provided numerous novel proposals for innovative design. Meanwhile, it resulted in an explosion of alternatives. Choosing a small amount of the personalized recommendations is becoming more and more difficult, and engineers may prefer fewer goals. Furthermore, engineers may choose designs that have variety and feasibility in BID. However, the diversity and feasibility of BID are often contradictory. Engineers need to apply multi-objective optimization methods to find better BID to ensure implementable. Aiming to overcome the above problems, based on the biological strategies from the most popular BID-AskNature, we proposed an interdisciplinary approach with the bidirectional encoder representation from transformers (BERT) and Pareto dominance for biological strategy decision (BPBSD) in the BID. First, we use BERT to find potential biological strategies similar to the keyword of BID. Then, aiming at the functional diversity and the feasibility of BID, we used the Pareto dominance theory to solve the contradiction. Finally, we verify the effectiveness of the proposed approach with three different application cases. Experiments show that the proposed BPBSD can balance the relationship between diversity and feasibility in BID. It is hoped that this work provides practical guidance for BID.     

Description and applications of a computer program for control systems design

A General, Multipurpose Simulation and Control PackagE (GEMSCOPE) is described which assists with the design, analysis and digital simulation of linear, time-invariant dynamic systems. The process model may be defined in block diagram form with transfer functions for each component, or in standard state-space form. Options are included for generating the optimal regulatory control matrices, determining the state feedback matrix for non-interaction, reducing the order of the model, and calculating the open or closed-loop system response. GEMSCOPE has proven useful for student education and for the design of practical control systems. Experimental and simulated data obtained by applying GEMSCOPE techniques to computer controlled pilot plants at the University of Alberta demonstrate the validity of the methods and the significant improvements possible through the use of modern control techniques.     

RF energy harvesting systems: An overview and design issues

One of the main challenges in implementing sensor devices for internet of things (IoTs), is the means for the operating power supply. RF energy harvesting (RFEH) presents a promising solution as RF power is a suitable choice particularly for cases where solar harvesting is not feasible. However, in spite of RF communication system design being a well‐established, there are several challenges poised for the implementation of the RFEH systems especially for harvesting the ambient RF signals. The challenges can be widely categorized as the overall conversion efficiency, bandwidth, and form factor. In this article, an exhaustive survey on the different RFEH system that is reported is carried out and discussed. Important design issues are identified with insights drawn. First, we have presented the challenges in designing antennas for RFEH systems. This is followed by rectifier circuits and matching networks, and eventually a general frame work for designing of ambient RFEH systems is deduced.     

Performance comparison of bio-inspired optimization algorithms for Sierpinski gasket fractal antenna design

The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna.     

BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.     

Utility and mechanism design in multi-agent systems: An overview

Future cities promise to be more autonomous than ever, largely owing to our ability of coordinating complex systems in real time: fleets of self-driving cars will offer on-demand transportation services, delivery drones will fly parcels in our skies, power plants will provide renewable energy reliably. In many of these systems, there is no single decision-maker with full information and authority. Instead, the system performance greatly depends on the decisions made by interacting entities with local information and limited communication capabilities. Game theory, intended as the study of multi-agent decision-making, is a fitting paradigm to tackle many of the associated challenges. Moving from this observation, in this paper we review how tools and ideas from game theory can be brought to bear on the coordination of multi-agent systems. At the heart of the proposed approach is the design and influence of agents’ preferences so that their local optimization induces a desirable system behavior. Its applicability spans a variety of settings irrespective of whether the decision makers are strategic (e.g., drivers in a road network), or not (e.g., delivery drones). Along the way, we also discuss future research directions and connections with related research areas including algorithmic game theory, incentive and mechanism design, economics, computational complexity, and approximation algorithms.     

Design of a bio-inspired contact-aided compliant wrist

This paper reports about the design of a bio-inspired compliant wrist, whose mobility (i.e. ulnar-radial deviation and flexion-extension) has been realized by employing two pairs of contact-aided Cross-Axis Flexural Pivots (CAFPs), actuated via remotely-placed servo-motors and tendon transmissions. The human wrist behaves differently when deflecting in clockwise or anticlockwise direction, both in terms of maximum angular deflection and passive stiffness. The device proposed hereafter aims at mimicking such natural asymmetry, while withstanding unexpected external loads. In order to fulfill these requirements, two contacts are included: (i) a pure rolling contact (named passive contact), achieved via a cam mechanism guiding the CAFP deflection and ensuring the wrist resistance to compressive loads; (ii) a purposely shaped contact pair (named active contact), acting on one beam of the CAFP so as to increase its stiffness. The design procedures and tools specifically developed for the wrist optimization are described. In the first step, a CAFP shape optimization is performed, followed by the synthesis of the active contact pair. In the second step, the centrodes are computed and then used to generate the passive contact profiles. At last, the third step focuses on the definition of the tendons routing. To prove the validity of the numerical models, a physical prototype of the wrist is produced and tested. Direct comparisons between simulations and experiments confirm the efficacy of the proposed design method.     

Estimation of fog utility pricing: a bio-inspired optimisation techniques' perspective

ABSTRACT

Due to the limited data storage capacity available to Internet service providers and large-scale enterprises, the concept of resource sharing arises. The services can be given on lease to enterprises through Service Level Agreements. Being the extension of the cloud computing, fog computing architecture brings the resources near end users. In order to get the services on lease, the enterprises are supposed to pay for the resources or services which are being used by them. In this paper, four nature inspired algorithms are analysed in order to determine the efficient management of services or resources so that the cost of resources can be reduced and the billing can be attained through calculation of the utilised resources. Pigeon inspired optimization, enhanced differential evolution, binary bat algorithm and simple human learning optimization are used to evaluate the energy consumed by the edge nodes or cloudlets that in turn can be used for estimating the bill through the Time of Use pricing variable. We evaluate the aforementioned techniques to analyse their performance regarding the bill calculation on the basis of fog servers usage. Simulation results demonstrate that BAT algorithm gives significantly better results than other three algorithms in terms of resource utilisation and bill reduction.     

ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems

In this paper, a hybrid bio-inspired metaheuristic optimization approach namely emperor penguin and salp swarm algorithm (ESA) is proposed. This algorithm imitates the huddling and swarm behaviors of emperor penguin optimizer and salp swarm algorithm, respectively. The efficiency of the proposed ESA is evaluated using scalability analysis, convergence analysis, sensitivity analysis, and ANOVA test analysis on 53 benchmark test functions including classical and IEEE CEC-2017. The effectiveness of ESA is compared with well-known metaheuristics in terms of the optimal solution. The proposed ESA is also applied on six constrained and one unconstrained engineering problems to evaluate its robustness. The results reveal that ESA offers optimal solutions as compared to the other competitor algorithms.

     

Surface characterization and friction of a bio-inspired reversible adhesive tape

A polyvinylsilixane sample fabricated to replicate biological attachment systems consisting of microscale pillars (about 230/mm²) approximately 50 μm in diameter, 70 μm in height, and 60 μm center-to-center was characterized for surface roughness, friction force, and contact angle and compared to an unstructured sample. Macroscale coefficient of kinetic friction of the structured sample was found to be almost four times greater than the unstructured sample. This increase was determined to be a result of the structured roughness of the sample and not the random nanoroughness. The structured roughness also increased the hydrophobicity of the structured sample, which is important for self cleaning to occur.     

On the design of hybrid bio-inspired meta-heuristics for complex multiattribute vehicle routing problems

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta-heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as main approach and GA as local search. GA-ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO-GA approach outperforms the others. Considering the cost, ACO, GA, and GA-ACO provide better results. Regarding computation time, GA and GA-ACO have been found the most competitive among the benchmark.