This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.

No description available.

No description available.

No description available.

No description available.

With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. For software testing, a new simulation-based internal variable range coverage metric is proposed with the goal of enhancing the error detection capability of the generated test data when applied as the target metric. For software model checking, we utilize various dynamic analysis methods, such as data mining, swarm intelligence (ant colony optimization), to extract useful high-level information from program execution data. Despite being incomplete, dynamic program execution can still help to uncover important program structure features and variable correlations. The extracted knowledge, such as invariants in different forms, promising control flows, etc., is then used to facilitate code-level program abstraction (under-approximation/over-approximation), and/or state space partition, which in turn improve the performance of property verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. Experimental results demonstrated the effectiveness of our methods in improving the quality as well as performance of software verification. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing — a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods. / Ph. D.

Integrated circuits, from general purpose microprocessors to application specific designs (ASICs), have become ubiquitous in modern technology. As our applications have become more complex, so too have the circuits used to drive them. Moore's law predicts that the number of transistors on a chip doubles every 18-24 months. This explosion in circuit size has also lead to significant growth in testing effort required to verify the design. In order to cope with the required effort, the testing problem must be approached from several different design levels. In particular, exploiting the Register Transfer Level for test generation allows for the use of relational information unavailable at the structural level. This dissertation demonstrates several novel methods for generating tests applicable for both structural and functional tests. These testing methods allow for significantly faster test generation for functional tests as well as providing high levels of fault coverage during structural test, typically outperforming previous state of the art methods. First, a semi-formal method for functional verification is presented. The approach utilizes a SMT-based bounded model checker in combination with an ant colony optimization based search engine to generate tests with high branch coverage. Additionally, the method is utilized to identify unreachable code paths within the RTL. Compared to previous methods, the experimental results show increased levels of coverage and improved performance. Then, an ant colony optimization algorithm is used to generate high quality tests for fault coverage. By utilizing co-simulation at the RTL and gate level, tests are generated for both levels simultaneously. This method is shown to reach previously unseen levels of fault coverage with significantly lower computational effort. Additionally, the engine was also shown to be effective for behavioral level test generation. Next, an abstraction method for functional test generation is presented utilizing program slicing and data mining. The abstraction allows us to generate high quality test vectors that navigate extremely narrow paths in the state space. The method reaches previously unseen levels of coverage and is able to justify very difficult to reach control states within the circuit. Then, a new method of fault grading test vectors is introduced based on the concept of operator coverage. Operator coverage measures the behavioral coverage in each synthesizable statement in the RTL by creating a set of coverage points for each arithmetic and logical operator. The metric shows a strong relationship with fault coverage for coverage forecasting and vector comparison. Additionally, it provides significant reductions in computation time compared to other vector grading methods. Finally, the prior metric is utilized for creating a framework of automatic test pattern generation for defect coverage at the RTL. This framework provides the unique ability to automatically generate high quality test vectors for functional and defect level testing at the RTL without the need for synthesis. In summary, We present a set of tools for the analysis and test of circuits at the RTL. By leveraging information available at HDL, we can generate tests to exercise particular properties that are extremely difficult to extract at the gate level. / Ph. D. / Digital circuits and modern microprocessors are pervasive in modern life. The complexity and scope of these devices has dramatically increased to meet new demands and applications, from entertainment devices to advanced automotive applications. Rising complexity causes design errors and manufacturing defects are more difficult to detect and increases testing costs. To cope with rising test costs, significant effort has been directed towards automating test generation early in development when defects are less expensive to correct. Modern digital circuits are designed using Hardware Description Languages (HDL) to describe their behavior at a high logical level. Then, the behavioral description is translated to a chip level implementation. Most automated test tools use the implementation description since it is a more direct representation of the manufactured circuit. This dissertation demonstrates several methods to utilize available logical information in behavioral descriptions for generating tests early in development that maintain applicability throughout the design process. The proposed algorithms utilize a biologically-inspired search, the ant colony optimization, abstracting test generation as an ant colony hunting for food. In the abstraction, a sequence of inputs to a circuit is represented by the walked path of an individual ant and untested portions of the circuit description are modelled as food sources. The final test is a collection of paths that efficiently reach the most food sources. Each algorithm also explores different software analysis techniques, which have been adapted to handle unique constraints of HDLs, to learn about the target circuits. The ant colony optimization uses the analysis to help guide and direct the search, yielding more efficient execution than prior techniques and reducing the time required for test generation. Additionally, the described methods can automatically generate tests in cases previously requiring manual generation, improving overall test quality.

Fire ants link their legs and jaws together to form functional structures called self- assemblages. Examples include floating rafts, towers, bridges, and bivouacs. We investigate these self-assemblages of fire ants. Our studies are motivated in part by the vision of providing guidance for programmable robot swarms. The goal for such systems is to develop a simple programmable element from which complex patterns or behaviors emerge on the collective level. Intelligence is decentralized, as is the case with social insects such as fire ants. In this combined experimental and theoretical study, we investigate the construction of two fire ant self-assemblages that are critical to the colony’s survival: the raft and the tower. Using time-lapse photography, we record the construction processes of rafts and towers in the laboratory. We identify and characterize individual ant behaviors that we consistently observe during assembly, and incorporate these behaviors into mathematical models of the assembly process. Our models accurately predict both the assemblages’ shapes and growth patterns, thus providing evidence that we have identified and analyzed the key mechanisms for these fire ant self-assemblages. We also develop novel techniques using scanning electron microscopy and micro-computed tomography scans to visualize and quantify the internal structure and packing properties of live linked fire ants. We compare our findings to packings of dead ants and similarly shaped granular material packings to understand how active arranging affects ant spacing and orientation. We find that ants use their legs to increase neighbor spacing and hence reduce their packing density by one-third compared to packings of dead ants. Also, we find that live ants do not align themselves in parallel with nearest neighbors as much as dead ants passively do. Our main contribution is the development of parsimonious mathematical models of how the behaviors of individuals result in the collective construction of fire ant assemblages. The models posit only simple observed behaviors based on local information, yet their mathe- matical analysis yields accurate predictions of assemblage shapes and construction rates for a wide range of ant colony sizes.

In swarm robotics, the control of a group of robots is often fully distributed and does not rely on any leader. In this thesis, we are interested in understanding how to design collective decision making processes in such groups. Our approach consists in taking inspiration from nature, and especially from self organization in social insects, in order to produce effective collective behaviors in robot swarms. We have devised four robotics experiments that allow us to study multiple facets of collective decision making. The problems on which we focus include cooperative transport of objects, robot localization, resource selection, and resource discrimination. <p><p>We study how information is transferred inside the groups, how collective decisions arise, and through which particular interactions. Important properties of the groups such as scalability, robustness, and adaptivity are also investigated. We show that collective decisions in robot swarms can effectively arise thanks to simple mechanisms of imitation and amplification. We experimentally demonstrate their implementation with direct or indirect information transfer, and with robots that can distinguish the available options partially or not at all. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

We investigate the use of self-assembly in a robotic system as a means of responding<p>to different environmental contingencies. Self-assembly is the mechanism through which<p>agents in a multi-robot system autonomously form connections with one another to create<p>larger composite robotic entities. Initially, we consider a simple response mechanism<p>that uses stochastic self-assembly without any explicit control over the resulting morphology<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished