416 research outputs found
Traveling Salesman Problem
The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance
Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things
(IoT) systems, e.g., smart farms, as a data collection platform. However, the
UAV-IoT wireless channels may be occasionally blocked by trees or high-rise
buildings. An intelligent reflecting surface (IRS) can be applied to improve
the wireless channel quality by smartly reflecting the signal via a large
number of low-cost passive reflective elements. This article aims to minimize
the energy consumption of the system by jointly optimizing the deployment and
trajectory of the UAV. The problem is formulated as a
mixed-integer-and-nonlinear programming (MINLP), which is challenging to
address by the traditional solution, because the solution may easily fall into
the local optimal. To address this issue, we propose a joint optimization
framework of deployment and trajectory (JOLT), where an adaptive whale
optimization algorithm (AWOA) is applied to optimize the deployment of the UAV,
and an elastic ring self-organizing map (ERSOM) is introduced to optimize the
trajectory of the UAV. Specifically, in AWOA, a variable-length population
strategy is applied to find the optimal number of stop points, and a nonlinear
parameter a and a partial mutation rule are introduced to balance the
exploration and exploitation. In ERSOM, a competitive neural network is also
introduced to learn the trajectory of the UAV by competitive learning, and a
ring structure is presented to avoid the trajectory intersection. Extensive
experiments are carried out to show the effectiveness of the proposed JOLT
framework.Comment: 11 pages, 7 figures, 4 table
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
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
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