A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP

Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots’ workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Combining audio-based similarity with web-based data to accelerate automatic music playlist generation

We present a technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently published interface for music players that benefits from intelligent structuring of audio collections. While the original approach involves the calculation of similarities between every pair of songs in a collection, we incorporate web-based data to reduce the number of necessary similarity calculations. More precisely, we exploit artist similarity determined automatically by means of web retrieval to avoid similarity calculation between tracks of dissimilar and/or unrelated artists. We evaluate our acceleration technique on two audio collections with different characteristics. It turns out that the proposed combination of audio- and text-based similarity not only reduces the number of necessary calculations considerably but also yields better results, in terms of musical quality, than the initial approach based on audio data only. Additionally, we conducted a small user study that further confirms the quality of the resulting playlists

Fine-Grained Complexity of k-OPT in Bounded-Degree Graphs for Solving TSP

The Traveling Salesman Problem asks to find a minimum-weight Hamiltonian cycle in an edge-weighted complete graph. Local search is a widely-employed strategy for finding good solutions to TSP. A popular neighborhood operator for local search is k-opt, which turns a Hamiltonian cycle C into a new Hamiltonian cycle C\u27 by replacing k edges. We analyze the problem of determining whether the weight of a given cycle can be decreased by a k-opt move. Earlier work has shown that (i) assuming the Exponential Time Hypothesis, there is no algorithm that can detect whether or not a given Hamiltonian cycle C in an n-vertex input can be improved by a k-opt move in time f(k) n^o(k / log k) for any function f, while (ii) it is possible to improve on the brute-force running time of O(n^k) and save linear factors in the exponent. Modern TSP heuristics are very successful at identifying the most promising edges to be used in k-opt moves, and experiments show that very good global solutions can already be reached using only the top-O(1) most promising edges incident to each vertex. This leads to the following question: can improving k-opt moves be found efficiently in graphs of bounded degree? We answer this question in various regimes, presenting new algorithms and conditional lower bounds. We show that the aforementioned ETH lower bound also holds for graphs of maximum degree three, but that in bounded-degree graphs the best improving k-move can be found in time O(n^((23/135+epsilon_k)k)), where lim_{k -> infty} epsilon_k = 0. This improves upon the best-known bounds for general graphs. Due to its practical importance, we devote special attention to the range of k in which improving k-moves in bounded-degree graphs can be found in quasi-linear time. For k <= 7, we give quasi-linear time algorithms for general weights. For k=8 we obtain a quasi-linear time algorithm when the weights are bounded by O(polylog n). On the other hand, based on established fine-grained complexity hypotheses about the impossibility of detecting a triangle in edge-linear time, we prove that the k = 9 case does not admit quasi-linear time algorithms. Hence we fully characterize the values of k for which quasi-linear time algorithms exist for polylogarithmic weights on bounded-degree graphs

Controlling the mobility and enhancing the performance of multiple message ferries in delay tolerant networks

In einem drahtlosen Netzwerk mit isolierten und stationären Knoten können Adhoc und verzögerungstolerante Netzwerk Routing-Protokolle nicht verwendet werden. Message Ferry Netzwerke sind die Lösung für diese Fälle, in denen ein (oder mehrere) Message Ferry Knoten den store-carry-forward Mechanismus verwendet und zwischen den Knoten reist, um Nachrichten auszutauschen. In diesem Fall erfahren die Nachrichten für gewöhnlich eine lange Verzögerung. Um die Performance der Message Ferry Netzwerke zu verbessern, kann die Mobilität der Message Ferry Knoten gesteuert werden. In dieser Doktorarbeit werden zwei Strategien zur Steuerung der Mobilität der Message Ferry Knoten studiert. Die Strategien sind das on-the-fly Entscheidungsverfahren in Ferry Knoten und die offline Wegplanung für Ferry Knoten. Für die on-the-fly Strategie untersucht diese Arbeit Decision-maker in Ferry Knoten, der die Entscheidung auf Grundlage der lokalen Observation eines Ferry Knoten trifft. Zur Koordinierung mehrerer Ferry Knoten, die keine globale Kenntnis über das Netzwerk haben, wird eine indirekte Signalisierung zwischen Ferry Knoten vorgeschlagen. Zur Kooperation der Ferry Knoten für die Zustellung der Nachrichten werden einige Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten vorgeschlagen, in denen der Decision-maker eines Ferry Knotens seine Information mit dem verzögerungstoleranten Router des Ferry Knoten teilt, um die Effizienz des Nachrichtenaustauschs zwischen Ferry Knoten zu verbessern. Umfangreiche Simulationsstudien werden zur Untersuchung der vorgeschlagenen Ansätze und des Einflusses verschiedener Nachrichtenverkehrsszenarien vorgenommen. Außerdem werden verschiedene Szenarien mit unterschiedlicher Anzahl von Ferry Knoten, verschiedener Geschwindigkeit der Ferry Knoten und verschiedener Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten studiert. Zur Evaluierung der offline Wegplanungsstrategie wird das Problem als Multiple Traveling Salesmen Problem (mTSP) modelliert und ein genetischer Algorithmus zur Approximation der Lösung verwendet. Es werden verschiedene Netzwerkarchitekturen zur Pfadplanung der Ferry Knoten vorgestellt und studiert. Schließlich werden die Strategien zur Steuerung der Mobilität der Ferry Knoten verglichen. Die Ergebnisse zeigen, dass die Performance der Strategien in Bezug auf die Ende-zu-Ende-Verzögerung von dem Szenario des Nachrichtenverkehrs abhängt. In Szenarien, wie Nachrichtenverkehr in Sensor-Netzwerken, in denen ein Knoten die Nachrichten zu allen anderen Knoten sendet oder von allen anderen Knoten empfängt, zeigt die offline Wegplanung, basierend auf der mTSP Lösung, bessere Performance als die on-the-fly Strategie. Andererseits ist die on-the-fly Stratgie eine bessere Wahl in Szenarien wie Nachrichtenaustausch zwischen Rettungskräften während einer Katastrophe, in denen alle drahtlose Knoten die Nachrichten austauschen müssen. Zudem ist die on-the-fly Strategie flexibler, robuster als offline Wegplanung und benötigt keine Initialisierungszeit.In a wireless network with isolated and stationary nodes, ad hoc and delay tolerant routing approaches fail to deliver messages. Message ferry networks are the solution for such networks where one or multiple mobile nodes, i.e. message ferry, apply the store-carry-forward mechanism and travel between nodes to exchange their messages. Messages usually experience a long delivery delay in this type of network. To improve the performance of message ferry networks, the mobility of ferries can be controlled. In this thesis, two main strategies to control mobility of multiple message ferries are studied. The strategies are the on-the-fly mobility decision making in ferries and the offline path planning for ferries. To apply the on-the-fly strategy, this work proposes a decision maker in ferries which makes mobility decisions based on the local observations of ferries. To coordinate multiple ferries, which have no global view from the network, an indirect signaling of ferries is proposed. For cooperation of ferries in message delivery, message forwarding and replication schemes are proposed where the mobility decision maker shares its information with the delay tolerant router of ferries to improve the efficiency of message exchange between ferries. An extensive simulation study is performed to investigate the performance of the proposed schemes and the impact of different traffic scenarios in a network. Moreover, different scenarios with different number of ferries, different speed of ferries and different message exchange approaches between ferries are studied. To study the offline path planning strategy, the problem is modeled as multiple traveling salesmen problem (mTSP) and a genetic algorithm is applied to approximate the solution. Different network architectures are proposed and studied where the path of ferries are planned in advance. Finally, the strategies to control the mobility of ferries are compared. The results show that the performance of each strategy, in terms of the average end-to-end delay of messages, depends on the traffic scenario in a network. In traffic scenarios same as the traffic in sensor networks, where only a single node generates messages to all nodes or receives messages from all node, the offline path planning based on mTSP solution performs better than the on-the-fly decision making. On the other hand, in traffic scenarios same as the traffic in disaster scenarios, where all nodes in a network may send and receive messages, the on-the-fly decision making provides a better performance. Moreover, the on-thy-fly decision making is always more flexible, more robust and does not need any initialization time