Optimization Algorithm’s Problems: Comparison Study

Currently, in various fields and disciplines problem optimization are used commonly. In this concern, we have to define solutions which are two known concepts optimal or near optimal optimization problems in regards to some objects. Usually, it is surely difficult to sort problems out in only one step, but some processes can be followed by us which people usually call it problem solving. Frequently, the solution process is split into various steps which are accomplishing one after the other. Therefore, in this paper we consider some algorithms that help us to sort out problems, for exemplify, finding the shortest path, minimum spanning tree, maximum network flows and maximum matching. More importantly, the algorithm comparison will be presented. Additionally, the limitation of each algorithm. The last but not the least, the future research in this area will be approached

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account

Parameterized synthesis of self-stabilizing protocols in symmetric networks

Self-stabilization in distributed systems is a technique to guarantee convergence to a set of legitimate states without external intervention when a transient fault or bad initialization occurs. Recently, there has been a surge of efforts in designing techniques for automated synthesis of self-stabilizing algorithms that are correct by construction. Most of these techniques, however, are not parameterized, meaning that they can only synthesize a solution for a fixed and predetermined number of processes. In this paper, we report a breakthrough in parameterized synthesis of self-stabilizing algorithms in symmetric networks, including ring, line, mesh, and torus. First, we develop cutoffs that guarantee (1) closure in legitimate states, and (2) deadlock-freedom outside the legitimate states. We also develop a sufficient condition for convergence in self-stabilizing systems. Since some of our cutoffs grow with the size of the local state space of processes, scalability of the synthesis procedure is still a problem. We address this problem by introducing a novel SMT-based technique for counterexample-guided synthesis of self-stabilizing algorithms in symmetric networks. We have fully implemented our technique and successfully synthesized solutions to maximal matching, three coloring, and maximal independent set problems for ring and line topologies

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices. The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic - specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their “native” logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device. IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance. Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions. Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an “acceptable” average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were compared on a simple problem where the AAF target was changed during evolution, revealing that dynamically tracking the AAF target can yield a higher success rate in meeting the AAF. The combination of IDGP and FI offers a novel approach for achieving online learning with GP on highly resource-constrained embedded systems. Furthermore, it simultaneously considers the acceptable average performance of the system which may change during the operational lifetime. This approach could be applied to swarm and cooperative robot systems, WSN motes or IoT devices allowing them to cooperatively learn and adapt their logic locally to meet dynamic performance requirements

Controller Design and Optimization for Rotor System Supported by Active Magnetic Bearings

Active Magnetic Bearings (AMBs) have been receiving increased attention in industry because of the advantages (contact-free, oil-free, etc.,) that they display in comparison with conventional bearings. They are used extensively in rotor system applications, especially in conditions where conventional bearing systems fail. Most AMBs are controlled by Proportional-Integral-Derivative (PID)-controllers. Controller design for AMB systems by means of hand tuning is time-consuming and requires expert knowledge. In order to avoid this situation and reduce the effort to tune the controller, multi-objective optimization with genetic algorithm is introduced to design and optimize the AMB controllers. In the optimization, criteria both in time and frequency domain are considered. A hierarchical fitness function evaluation procedure is used to accelerate the optimization process and to increase the probability of convergence. This evaluation procedure guides the optimizer to locate the small feasible region resulting mainly from the requirement for stability of control system. Another strategy to reduce the number of optimization parameters is developed, which is based on a sensitivity analysis of the controller parameters. This strategy reduces directly the complexity of the optimization problem and accelerates the optimization process. Controller designs for two AMB systems are considered in this thesis. Based on the introduced and presented hierarchical evaluation strategy, the controller design for the first AMB system is obtained without specific requirements related to initial solutions. The optimal controller design is applied to a test rig with a flexible rotor supported by AMBs. The results show that the introduced optimization procedure realizes the desired results of the controlled system’s behavior. The maximal speed of 15000 rpm is reached. The second AMB system is designed for a turbo-compressor. The introduced parameter reduction strategy is applied for the controller design of this AMB system. The controller design is optimized in the search space around an initial solution. Optimization results show the efficiency of the introduced strategy.Aufgrund vieler Vorteile (wie z. B. Kontaktfreiheit, Ölfreiheit) gegenüber konventionellen Lagern etablieren sich aktive Magnetlager zunehmend in der Industrie. Aktive Magnetlager werden zum großen Teil in Rotorsystemen verwendet, wo konventionelle Öllager für die Anwendung versagen. PID-Regler werden häufig für Magnetlager verwendet. Die Auslegung des Reglers wird durch manuelle Einstellung (trial and error) bestimmt und ist sehr zeitaufwendig. Zudem bedarf es spezieller Fachkenntnisse zur Einstellung. Um diese Situation zu vermeiden und den Aufwand für die Reglerauslegung zu reduzieren, wird die Mehrzieloptimierung mit Genetischen Algorithmen in der vorliegenden Arbeit zur Optimierung des Reglerentwurfs eingesetzt. In der Optimierung werden die Zielfunktionen sowohl im Zeit- wie auch im Frequenzbereich definiert. Um den Optimierungsprozess zu beschleunigen und die Wahrscheinlichkeit der Konvergenz der Optimierung zu erhöhen, wird eine hierarchische Struktur zur Bewertung der Zielfunktionen eingeführt. Dies hilft dem Optimierer bei der Lokalisierung des kleinen zulässigen Bereichs, der im Wesentlichen aus der Anforderung an die Stabilität des Magnetlagersystems resultiert. Desweitern wird eine Strategie zur Reduzierung der Optimierungsparameter entwickelt, die auf der Sensitivitätsanalyse der Reglerparameter basiert. Diese Strategie reduziert die Komplexität des Optimierungsproblems und führt zu einer Beschleunigung des Optimierungsprozesses. In der vorliegenden Arbeit wird der Reglerentwurf von zwei Magnetlagersystemen berücksichtigt. Mit Hilfe der eingeführten Strategie zur Bewertung der Zielfunktionen, werden die Reglerparameter von dem ersten Magnetlagersystem bestimmt bzw. optimiert, ohne dass irgendeine Information über die Anfangslösung erforderlich ist. Der optimale Reglerentwurf wird dann in einem Versuchstand implementiert, in dem eine elastische Welle durch zwei Magnetlager gelagert ist. Die Versuchsergebnisse zeigen, dass das gewünschte dynamische Verhalten des geregelten Magnetlagersystems durch die Optimierung erzielt wird. Die maximal zulässige Drehzahl (15000 rpm) des Versuchsstandes wird mit dem optimalen Regler ohne Probleme erreicht. Als zweites Beispiel wird der Reglerentwurf eines magnetgelagerten Rotorsystems eines Turboverdichters betrachtet. In der Reglerauslegung wird die vorgeschlagene Optimierungsstrategie mit Hilfe von Parameterreduktion verwendet. Die optimale Lösung wird lokal in der Nähe einer Anfangslösung gesucht. Die Optimierungsergebnisse zeigen die Effizienz der Optimierungsstrategie

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way

Evolving distributed algorithms with genetic programming

Abstract—In this article, we evaluate the applicability of Genetic Programming (GP) for the evolution of distributed algorithms. We carry out a large-scale experimental study in which we tackle three well-known problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changing speed and messages taking over each other, i.e., out-of-order message delivery, occur with high probability. Second, we define extensions and adaptations of established GP approaches (such as treebased and Linear Genetic Programming) in order to make them suitable for representing distributed algorithms. Third, we introduce novel rule-based Genetic Programming methods designed especially with the characteristic difficulties of evolving algorithms (such as epistasis) in mind. Based on our extensive experimental study of these approaches, we conclude that GP is indeed a viable method for evolving non-trivial, deterministic, non-approximative distributed algorithms. Furthermore, one of the two rule-based approaches is shown to exhibit superior performance in most of the tasks and thus can be considered as an interesting idea also for other problem domains

Evolving distributed algorithms with genetic programming

In this paper, we present a detailed analysis of the applica-tion of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and coun-termeasures are provided. Six different Genetic Program-ming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly. Categories and Subject Descriptor