A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-linear Functions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others

Swarm intelligence algorithms adaptation for various search spaces

U današnje vrijeme postoji mnogo algoritama inteligencije rojeva koji se uspiješno koriste za rešavanje raznih teških problema optimizacije. Zajednicki elementi svih ovih algoritama su operator za lokalnu pretragu (eksploataciju) oko prona enih obecavajucih rješenja i operator globalne pretrage (eksploracije) koji pomaže u bijegu iz lokalnih optimuma. Algoritmi inteligencije rojeva obicno se inicijalno testiraju na neogranicenim, ogranicenim ili visoko-dimenzionalnim skupovima standardnih test funkcija. Nadalje, mogu se poboljšati, prilagoditi, izmijeniti, hibridizirati, kombinirati s lokalnom pretragom. Konacna svrha je korištenje takve metaheuristike za optimizaciju problema iz stvarnog svijeta. Domeni rješenja odnosno prostori pretrage prakticnih teških problema optimizacije mogu biti razliciti. Rješenja mogu biti vektori iz skupa realnih brojeva, cijelih brojeva ali mogu biti i kompleksnije strukture. Algoritmi inteligencije rojeva moraju se prilagoditi za razlicite prostore pretrage što može biti jednostavno podešavanje parametera algoritma ili prilagodba za cjelobrojna rješenja jednostavnim zaokruživanjem dobivenih realnih rješenja ali za pojedine prostore pretrage potrebnao je skoro kompletno prepravljanja algoritma ukljucujuci i operatore ekploatacije i ekploracije zadržavajuci samo proces vo enja odnosno inteligenciju roja. U disertaciji je predstavljeno nekoliko algoritama inteligencije rojeva i njihova prilagodba za razlicite prostore pretrage i primjena na prakticne probleme. Ova disertacija ima za cilj analizirati i prilagoditi, u zavisnosti od funkcije cilja i prostora rješenja, algoritme inteligencije rojeva. Predmet disertacije ukljucuje sveobuhvatan pregled postojecih implementacija algoritama inteligencije rojeva. Disertacija tako er obuhvaca komparativnu analizu, prikaz slabosti i snaga jednih algoritama u odnosu na druge zajedno s istraživanjem prilagodbi algoritama inteligencije rojeva za razlicite prostore pretrage i njihova primjena na prakticne problem. Razmatrani su problemi sa realnim rješenjima kao što su optimizacija stroja potpornih vektora, grupiranje podataka, sa cijelobrojnim rješenjima kao što je slucaj problema segmentacije digitalnih slika i za probleme gdje su rješenja posebne strukture kao što su problemi planiranja putanje robota i triangulacije minimalne težine. Modificirani i prilago eni algoritmi inteligencije rojeva za razlicite prostore pretrage i primjenih na prakticne probleme testirani su na standardnim skupovima test podataka i uspore eni s drugim suvremenim metodama za rješavanje promatranih problema iz literature. Pokazane su uspješne prilagodbe algoritama inteligencije rojeva za razne prostore pretrage. Ovako prilago eni algoritmi su u svim slucajevima postigli bolje rezultate u usporedbi sa metodama iz literature, što dovodi do zakljucka da je moguce prilagoditi algoritme inteligencije rojeva za razne prostore pretrage ukljucujuci i kompleksne strukture i postici bolje rezultate u usporedbi sa metodama iz literature

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy

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

Detection and Evaluation of Clusters within Sequential Data

Motivated by theoretical advancements in dimensionality reduction techniques we use a recent model, called Block Markov Chains, to conduct a practical study of clustering in real-world sequential data. Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees and can be deployed in sparse data regimes. Despite these favorable theoretical properties, a thorough evaluation of these algorithms in realistic settings has been lacking. We address this issue and investigate the suitability of these clustering algorithms in exploratory data analysis of real-world sequential data. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. In order to evaluate the determined clusters, and the associated Block Markov Chain model, we further develop a set of evaluation tools. These tools include benchmarking, spectral noise analysis and statistical model selection tools. An efficient implementation of the clustering algorithm and the new evaluation tools is made available together with this paper. Practical challenges associated to real-world data are encountered and discussed. It is ultimately found that the Block Markov Chain model assumption, together with the tools developed here, can indeed produce meaningful insights in exploratory data analyses despite the complexity and sparsity of real-world data.Comment: 37 pages, 12 figure