
Swarm Intelligence
... generated in a quest for local optimality” – Fred Glover and Manuel Laguna Or more simply: ...
... generated in a quest for local optimality” – Fred Glover and Manuel Laguna Or more simply: ...
WPEssink CARV 2013 V4.2
... [10] as well as many more. Genetic algorithms were first used to solve the travelling salesman problem in 1985 by Brady [11]. This was later developed further by Goldberg and Linge [12]. A full review on using genetic algorithms for the travelling salesman problem was done by Larrañaga in 1999 [13]. ...
... [10] as well as many more. Genetic algorithms were first used to solve the travelling salesman problem in 1985 by Brady [11]. This was later developed further by Goldberg and Linge [12]. A full review on using genetic algorithms for the travelling salesman problem was done by Larrañaga in 1999 [13]. ...
Module 35
... We are interested in relative hardness results BECAUSE of our inability to prove absolute hardness results That is, if we could prove strong absolute hardness results, we would not be as interested in relative hardness results ...
... We are interested in relative hardness results BECAUSE of our inability to prove absolute hardness results That is, if we could prove strong absolute hardness results, we would not be as interested in relative hardness results ...
Module 13 • Studying the internal structure of REC, the
... (typically referred to as P, not Poly) Major goal: Identify whether or not a problem belongs to Poly ...
... (typically referred to as P, not Poly) Major goal: Identify whether or not a problem belongs to Poly ...
Elements of Optimal Control Theory Pontryagin’s Maximum Principle
... velocity). The lander is equipped with a braking system, which applies a force u, in order to slow down the descent. The braking force cannot exceed a given (maximum) value U . Let x(t) be the distance from the landing site. Then Newton’s Law gives mẍ = −mg − k ẋ + u. The objective is to find the ...
... velocity). The lander is equipped with a braking system, which applies a force u, in order to slow down the descent. The braking force cannot exceed a given (maximum) value U . Let x(t) be the distance from the landing site. Then Newton’s Law gives mẍ = −mg − k ẋ + u. The objective is to find the ...
Introduction to Randomized Algorithms.
... Application of Yao’s Lemma Lemma: A lower bound on the expected number of probes required by any randomized algorithm to solve the Find-bill problem is (n + 1)/2. Proof: We assume that the bill is located in any one of the n boxes uniformly at random. We only consider deterministic algorithms that ...
... Application of Yao’s Lemma Lemma: A lower bound on the expected number of probes required by any randomized algorithm to solve the Find-bill problem is (n + 1)/2. Proof: We assume that the bill is located in any one of the n boxes uniformly at random. We only consider deterministic algorithms that ...
Travelling salesman problem
The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.TSP is a special case of the travelling purchaser problem and the Vehicle routing problem.In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. Thus, it is possible that the worst-case running time for any algorithm for the TSP increases superpolynomially (perhaps, specifically, exponentially) with the number of cities.The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved completely and even problems with millions of cities can be approximated within a small fraction of 1%.The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as DNA sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. The TSP also appears in astronomy, as astronomers observing many sources will want to minimise the time spent slewing the telescope between the sources. In many applications, additional constraints such as limited resources or time windows may be imposed.