Swarm Intelligence

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more ...

In Computer Vision tracking of moving object in a real life scene is considered as a very challenging problem. Many factors such as illumination of light, noise, occlusion, sudden start and stop of moving object, shading etc. are associated with the task of tracking an object in a real scene under dynamic background(varying background) as well as static background(fixed background). In this paper we present a new object tracking algorithm using Quantum particle swarm optimization (QPSO).QPSO essentially deals with the dominant points of an object to be tracked. The novelty of our approach is that QPSO with a set of dominant points as stated above can be successfully applied for object tracking with both variable background as well as static background. Thus a unified attempt has been made for object tracking in a real life scene. In our approach we first detect the dominants points of objects to be tracked, then a group of particles form a swarm are initialized randomly over the image search space. It start searching the curvature connected between two consecutive dominant points until they satisfy a fitness criterion. As the curvature moves and the curvature movement is tracked by the swarm throughout the video and eventually when the swarm reaches optimal solution, a bounding box drawn based on particles final position. Experimental results demonstrate that this proposed QPSO based method works efficiently and effectively for object tracking in both dynamic and static environments. A comparative study shows that QPSO based tracking algorithm, on an average, runs 90% faster than PSO based tracking algorithm.

La plateforme opensource MyWebIntelligence (ou MyWI,) est un projet de recherche produit par le laboratoire MICA dans le cadre de l’Institut des Humanités Digitales qui vise à fournir un outil stratégique dans l’analyse et l’intelligence de la communication sur Internet.

Il s’agit d’un « crawler » d’une nouvelle génération qui constitue, à partir d’un dictionnaire de mots clés, une base de données des pages web qualifiées au service de l’intelligence stratégique. Il intégrera non seulement de nombreuses sources de données externes et Open Data mais aussi les derniers algorithmes de qualification de données (SNA, TextAnalysis, etc.)

Le CHU de Bordeaux a souhaité participer aux bêta tests de cette plateforme autour d’un projet pilote qui vise à mettre en place une étude débouchant sur une veille du discours thérapeutique et affilié. En effet le web a bouleversé l’ordre du discours médical. Première source d’information du patient et des accompagnants, il est un espace non structuré voire anarchique de prise de parole où la légitimité des savoirs et la hiérarchisation des connaissances s’avèrent difficiles. Pourtant ces « savoirs » sont aujourd’hui de plus en plus prégnant dans l’univers du malade.

MyWebIntelligence peut fournir les moyens d’appréhender, de qualifier et de hiérarchiser une masse de discours considérable afin de cartographier l’univers discursif de l’information médicale pédiatrique. L’ensemble du corps médical pourra ainsi non seulement avoir des études en temps réel sur le discours thérapeutique en ligne mais aussi mieux appréhender les acteurs hétérogènes du savoir et leurs stratégies argumentaires.

Dans la société digitale, si le médecin n’est plus seul dans la prescription médicale, le médecin 2.0 reprend une place prépondérante dans la diffusion de la prescription scientifique.

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

ABSTRACT The knight's tour problem is a sub chess puzzle where the objective of the puzzle is to find combination moves made by a knight so that it visits every square of the chessboard exactly once. This paper proposes a model using Binary Magnetic Optimization Algorithm to solve the problem. Each particle represents a possible solution of the problem. A pair of 3 binary bits is used to represent a move made by the knight for each move. The numerical value for each move can be in the range of 0 to 7 according to the 8 possible moves that can be taken by the knight. The fitness of a particle is then calculated based on the number of legal moves it has taken starting from the first move. The proposed model has been tested using the standard 8x8 chessboard setup. A comparative performance study is done based on two updating rules of Binary Magnetic Optimization Algorithm: sigmoid & tanh functions. Results obtained show that the proposed model has a potential for further improvement.

ABSTRACT Slope stability analysis of soil with a weak layer sandwiched between two strong layers is considered as a complex geotechnical problem. In this problem, the objective function is non-convex and discontinuous with the presence of multiple strong local minima. Classical optimization techniques fail to converge to a valid solution unless a proper initial trial is adopted. Even though many new optimization algorithms have emerged, they have not been applied to geotechnical problems yet. In the present study, some recent swarm intelligence algorithms are adopted for some complicated example of slope stability problems and benchmarked with the traditional particle swarm optimization algorithm. From the results, it seems the levy flight krill herd algorithm is the most efficient method over proposed algorithms for this kind of problem. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents an optimized K-nearest neighbors (KNNs) classification algorithm using the metaheuristic whale optimization to searches for sink node in wireless sensor networks. Sink node aggregate data from all sensor nodes and reducing the energy consumption network to prolong network lifetime. To reach aforementioned, a fitness function has formulated to choose the best location of sink node with high residual neighbor's sensor nodes energy to leads to maximizing the network lifetime. Eventually, the experimental results have been conducted whereas sensor nodes are propagated in a random location within the desired network area. The system has 11% improvement on the net-work's energy consumption that increases the lifetime of the network.

This paper presents an application of swarm intelligence towards the problem of spatial configuration. The methodology classifies activities as discrete entities, which self-organise topologically through associational parameters: an investigation of emergent route formation and spatial connectivity based on simple agent and pheromone interaction, coupled with the problem of ?loose? rectangular geometric assembly. A concept model sniffingSpace (Ireland, 2009) developed in Netlogo (Willensky, 1999), which established the self-organising topological capacity of the system, is extended in Processing (Fry & Rea, 2009) to incorporate rectangular geometry towards the problem of planning architectural space.

Many studies have focused in designing a set of good DNA sequences as it is one of the crucial tools in improving the reliability and efficiency of DNA computing. In this paper, an improved model of Ant Colony System is developed in optimizing DNA sequences design. The proposed model suggests that each artificial ant represents a possible solution of the

Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is employed to enhance the exploitation capability of SCOA. Simulation results show the proposed hybrid algorithm has successfully found the lowest-energy structures of the LJ2-LJ5, LJ7-LJ9 and LJ12 comparing with PSO and the standard SCOA.

Swarm based optimization algorithms are a collection of intelligent techniques in the field of Artificial Intelligence (AI) were developed for simulating the intelligent behavior of animals. Over the years ago, problems complexity increased in a means that it is very difficult for basic mathematical approaches to obtain an optimum solution in an optimal time, this leads the researchers to develop various algorithms base on the natural behaviors of living beings for solving problems. This paper present a review for Cat Swarm Optimization (CSO), which is a powerful metaheuristic swarmbased optimization algorithm inspired by behaviors of cats in the Nature for solving optimization problems. Since its first appearances in 2006, CSO has been improved and applied in different fields by many researchers. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed.

Biologically inspired cognitive systems have successfully been used for various purposes in recent years. There is, however, a considerable amount of work to be done using certain biological organisms to understand cognitions, especially in cases in which biological structures have been employed to create useful computational models that nevertheless are not thought to be explanations of cognitive capabilities. Superorganisms are at the basis of the swarm intelligence approach to artificial intelligence and robotics, but are also likely to lend themselves to the investigation of emergent phenomena involved in biology and in the foundation of cognitive capabilities. In this paper, I shall try to formulate hypotheses on some peculiar features of superorganisms that may help to shed light on understanding and building cognitive (artificial and natural) entities.

Edge detection is a fundamental procedure in image processing , machine vision, and computer vision. Its application area ranges from astronomy to medicine in which isolating the objects of interest in the image is of a significant importance. However, performing edge detection is a non-trivial task for which a large number of techniques have been proposed to solve it. This paper investigates the use of Ant Colony Optimization — a prominent set of optimization heuristics — to solve the edge detection problem. We propose two modified versions of the algorithm Ant Colony System (ACS) for an efficient and a noise-free edge detection.

– The Android Operating System covers almost 80% of the smartphones market. However, being so successful and popular amongst its users, Android Platform may misbehave in spite of all the rigorous security mechanisms it can afford. In our preceding work, we tried to state the analysis of malwares and Android malwares, how device application may be affected due to the malwares injected along with the Android Application Package. Static and Dynamic analysis incorporates the features extracted to define the proper functioning of the application. In this paper a packet capture cum classifier framework has been projected which will be a security mechanism for Android and is on the verge of implementation. In near future, our mechanism will sniff and capture the network transportation and determine the suspiciousness of the packets exchanged between Android devices or on the network and catalogue them as benign or suspicious/malicious applications. Our later work would be a framework for Android applications for recognition of Android packet misbehavior grounded on the features concluded from the procedure of analysis and thereby categorize them as benign or suspicious/malicious.

A swarm is defined as a set of two or more independent heterogeneous or homogeneous agents all acting in a common environment in a coherent fashion. This generates an emergent behaviour, which defines the behaviour of the swarm resulting from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificial swarms or robotic swarms has become an interesting topic of research in the last two decades. Many studies have been undertaken using a practical approach to swarm construction. Among these are studies investigating navigation and exploration tasks, task allocation, elementary construction, and communication. This paper develops theories and algorithms to support simple swarm robots in swarm robotics pattern formation, where complex behaviours can emerge from interactions between agents and each agent with the environment.

This paper investigates the research issues in routing protocols of the Wireless Sensor Networks (WSNs), since WSNs are becoming increasingly essential technology that would be used in a different type of applications such as home automation, physical monitoring, habitat monitoring, military applications, noise detection, and forest fire detection. The survey revealed that designing efficient routing protocols for sensor networks that suits sensor networks serving various applications is important. After reviewing many routing protocols the conclusion was that, swarm intelligence based routing protocols can eliminate at least one or more of the existing routing protocols limitations, such as, energy efficiency, scalability, mobility, deployment and fault tolerance.

Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. Optimization contributes to optimal resource management by way of efficient and effective problem-solving. Engineers' attention has been driven to more effective and scalable metaheuristic algorithms as a result of the complicated optimization issues. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. The authors see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores several algorithms such as ACO, PSO, GA, and FA.

This paper presents an optimization algorithm for tuning proportional-integral-derivative (PID) controller parameters in order to achieve a desired transient response. In order to find optimal PID controller parameters, it is rather a complicated task such that the best-proposed methods or techniques may lead to better performance. An artificial intelligence optimization approach is observed as a complementary solution to improve the performance of the PID controller designed by the conventional method. Hence, an offline tuning PID controller design scheme is proposed, which is able to deal with engineering problems, such that Social Spider Optimization (SSO) is chosen for this work. The step response of the controller was analyzed and described in terms of minimization of the system peak overshoot, Mp, steady-state error, ess, rise time, Tr and settling time, Ts. This algorithm able to obtain accurate solution and efficiency to solve such complex computational calculations especially for optimization problems as it has a fast convergence speed, results are very promising as optimal solutions to real-world problems are obtained.

Globalization rolls out the speed of market evolution is growing along with the severity of competition. New products appear daily. Retail and distribution are shifting to e-commerce. Changing market conditions impose increasingly stringent requirements on companies' business processes-speed and quality of execution, manageability, and adaptability. Realizing that corporations from the beginning of the century are constantly increasing IT investments. On a daily basis there's news on huge investments. Though there's a lack of reports on the success of these investments. Why is that? A long chain of sad accidents or an unknown pattern? On their way to the answer the authors use a new approach of social cybernetics to analyse problems of managing business processes of organizations. The fundamental limitation of modern approaches to the management and automation of business processes discovered as part of theoretical analysis. Further progress in enterprise IT requires a paradigm shift, and a return to a systematic, scientific approach which is opposed to the reliance on individual ingenious software developers.

This paper simulates the behavior of Cell Rovers, the cell sized bio- nanobots envisioned to be injected into Astronauts, by using a swarm of Kilobots. Alone, each Kilobot is unable to perform any meaningful function, but as a group they perform emergent behavior matching that of as Cell Rover built using nanomaterials called Dendrimers. The simulation captures the searching, movement, and protective behavior of a Cell Rover, but fails to simulate the repair of the damaged bodies. The paper explores and applies themes found in Particle Swarm Optimization, Ant Colony Optimization, and the Wolfpack Algorithm. It is found that the concept underlining the function of a Cell Rover is similar to a general optimization problem. The algorithm implemented simulates the function of a Cell Rover, but could also be used as an optimization algorithm where the strength of a chemical gradient across a search space represents the objective function. An example of such an application is to develop assembler nanobots that can actively repair damaged Mars-suit by operating as space-filling polyhedrons.