Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Energy Efficient Multipath Ant Colony Based Routing Algorithm for Mobile Ad hoc Networks

This paper describes the novel wireless routing protocol made for mobile ad hoc networks or wireless sensor networks using the bio-inspired technique. Bio-inspired algorithms include the routing capabilities taken from the social behavior of ant colonies, bird flocking, honey bee dancing, etc and promises to be capable of catering to the challenges posed by wireless sensors. Some of the challenges of wireless sensor networks are limited bandwidth, limited battery life, low memory, etc. An energy-efficient multipath routing algorithm based on the foraging nature of ants is proposed including many meta-heuristic impact factors to provide good robust paths from source to destination to overcome the challenges faced by resource-constrained sensors. Analysis of individual impact factor is represented which justifies their importance in routing performance. The multi-path routing feature is claimed by showing energy analysis as well as statistical analysis in-depth to the readers. The proposed routing algorithm is analyzed by considering various performance metrics such as throughput, delay, packet loss, network lifetime, etc. Finally, the comparison is done against AODV routing protocol by considering performance metrics where the proposed routing algorithm shows a 49% improvement in network lifetime

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application

Improvements of task scheduling and load balancing in cloud environment by swarm intelligence metaheuristics

Klaud racunarstvo pripada grupi novijih racunarskih paradigmi, koja se poput paradigme mrežnog racunarstva, bazira na grupisanju resursa i na korišcenju mrežnih i Internet tehnologija. U opštem smislu, klaud racunarstvo se odnosi na novi nacin isporuke racunarskih resursa u vidu usluge, gde se pod resursima podrazumeva gotovo sve, od podataka i softvera, do hardverskih komponenti, kao što su procesirajuci elementi, memorija i skladišta. Klaud racunarstvo je aktuelna i važna multidisciplinarna oblast, o cemu svedoci veliki broj objavljenih radova u vrhunskim me unarodnim casopisima i prikazanih na najznacajnijim svetskim skupovima. Na osnovu naucnih rezultata prikupljenih u objavljenim radovima iz ovog domena, može da se zakljuci da u klaud okruženju postoji veliki broj izazova i problema, za cije rešavanje mogu da se prona u bolje metode, tehnike i algoritmi. Jedan od najvažnijih izazova savremenog klaud okruženja je raspore ivanje zahteva krajnjih korisnika za izvršavanje na ogranicenom skupu raspoloživih resursa (virtuelnih mašina). Problem raspore ivanja na klaudu odnosi se na definisanje rasporeda izvršavanja zadataka na ogranicenom skupu raspoloživih resursa uzimajuci pritom u obzir potencijalna ogranicenja i funkciju cilja koju je potrebno optimizovati. Raspore ivanje poslova vrše algoritmi raspore ivanja, koji mogu da se podele na staticke i dinamicke. U slucaju statickog raspore ivanja, gde se poslovi ne mogu dinamicki prebacivati sa preopterecnih na manje opterecene virtuelne mašine, zadaci se raspore uju za izvršavanje na raspoložive virtuelne mašine pre pocetka izvršavanja. S druge strane, primenom metoda dinamickog raspore ivanja, koje je u literaturi poznato pod nazivom balansiranje opterecenja, vrši se preraspodela poslova izme u aktivnih virtuelnih mašina tokom samog izvršavanja programa raspore ivanja. Preraspodela se vrši tako što se zadaci sa virtuelnih mašina koje imaju vece opterecenje dinamicki prebacuju za izvršavanje na virtuelnim mašinama koje imaju manje opterecenje. Za potrebe dinamickog raspore ivanja koriste se uglavnom heuristicke i metaheuristicke optimizacione metode i algoritmi, koji postižu dobre rezultate. Problemi raspore ivanja poslova i balansiranja opterecenja na klaudu pripadaju grupi NP teških kombinatornih i/ili globalnih problema sa ili bez ogranicenja. Na osnovu publikovanih rezultata u relevantnim literaturnim izvorima, vidi se da su metaheuristike inteligencije rojeva, koje spadaju u grupu prirodom-inspirisanih algoritama, uspešno testirane na bencmark problemima i primenjivane na prakticnim NP teškim optimizacionim problemima (globalnim i kombinatornim) i da mogu da postignu bolje rezultate u smislu brzine konvergencije i kvaliteta rešenja, od drugih metoda, tehnika i algoritama. Polazeci od navedenog, u ovom radu je ispitivano da li je moguce dalje unaprediti rešavanja problema raspore ivanja poslova i balansiranja opterecenja na klaudu primenom metaheuristika inteligencije rojeva. Tokom sprovedenog istraživanja, unapre eno je i adaptirano više metaheuristika inteligencije rojeva za rešavanje problema raspore ivanja poslova i balansiranja opterecenja u klaud okruženju. U disertaciji su detaljno prikazane implementacije dva unapre ena algoritma rojeva - algoritma optimizacije monarh leptirovima i algoritma optimizacije jatom kitova. Za potrebe testiranja, rešavana su dva modela raspore ivanja poslova na klaudu. Prvi model, koji pripada grupi jednokriterijumske optimizacije, uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu, dok drugi, višekriterijumski model uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu i budžeta, tj. troškova za izvršavanje svih zahteva krajnjih korisnika. Simulacije su vršene u robusnom okruženju CloudSim simulatora i oba algoritma su testirana sa skupom veštackih podataka, generisanih u okviru CloudSim platforme, i realnih podataka, koji su preuzeti iz globalno dostupne bencmark baze. Osim testiranja za praktican izazov na klaudu, da bi se preciznije utvrdila unapre- enja modifikovanih metaheuristika u odnosu na osnovne verzije, obe metaheuristike su verifikovane i testiranjima na standardnim skupovima bencmark funkcija za globalnu optimizaciju bez ogranicenja. Upore ivanjem generisanih rezultata (kvalitet rešenja i brzina konvergencije) sa rezultatima najboljih poznatih metaheuristika i heuristika iz literature, koje su primenjivane na iste instance problema (na praktican problem raspore ivanja na klaudu i bencmark testove), dokazan je kvalitet implementiranih algoritama, cime je potvr ena i osnovna hipoteza ovog rada da se rešavanje izazova raspore ivanja poslova i balansiranja opterecenja u klaud okruženju mogu dalje unaprediti primenom metaheuristika inteligencije rojeva

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

Advanced Energy Harvesting Technologies

Energy harvesting is the conversion of unused or wasted energy in the ambient environment into useful electrical energy. It can be used to power small electronic systems such as wireless sensors and is beginning to enable the widespread and maintenance-free deployment of Internet of Things (IoT) technology. This Special Issue is a collection of the latest developments in both fundamental research and system-level integration. This Special Issue features two review papers, covering two of the hottest research topics in the area of energy harvesting: 3D-printed energy harvesting and triboelectric nanogenerators (TENGs). These papers provide a comprehensive survey of their respective research area, highlight the advantages of the technologies and point out challenges in future development. They are must-read papers for those who are active in these areas. This Special Issue also includes ten research papers covering a wide range of energy-harvesting techniques, including electromagnetic and piezoelectric wideband vibration, wind, current-carrying conductors, thermoelectric and solar energy harvesting, etc. Not only are the foundations of these novel energy-harvesting techniques investigated, but the numerical models, power-conditioning circuitry and real-world applications of these novel energy harvesting techniques are also presented

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Antenna Systems

This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling