Local search with k-exchange neighborhoods, k-opt, is the most widely used heuristic method for the traveling salesman problem (TSP). This paper presents an effective implementation of k-opt in LKH-2, a variant of the Lin–Kernighan TSP heuristic. The effectiveness of the implementation is demonstrated with experiments on Euclidean instances ranging from 10,000 to 10,000,000 cities. The runtime ...

this paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. first, the problem is formulated as a mixed integer linear programming model. then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. two experiments are carried out to evaluate th...

Multiple input multiple output (MIMO) symbol detection problem belongs to non-deterministic polynomial acceptable hard combinatorial optimization (CO) class. One of the key trials in design MIMO scheme is develop a low complexity algorithm without much compromise performance. Detection approaches proposed literature can be split into non-linear and linear algorithms. Vertical Bell-Labs Layered ...

We int rodu ce a new class of Markov chain Monte Carlo search pr ocedures tha t lead to mor e powerful optimization met hods than simulated annealing . The main idea is to embed det erminist ic local search techniques into stochas tic algorithms. The Mont e Carlo explores only local optima, and it is ab le to make large, global changes even at low temperatur es, t hus overcoming large barr iers...

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

Local search with k-change neighborhoods, k-opt, is the most widely used heuristic method for the traveling salesman problem (TSP). This report presents an effective implementation of k-opt for the LinKernighan TSP heuristic. The effectiveness of the implementation is demonstrated with extensive experiments on instances ranging from 10,000 to 10,000,000 cities.

In this paper we propose a heuristic algorithm to solve the Vehicle Routing Problem with Time Windows. Its framework is a smart combination of three simple procedures: the classical k-opt exchanges improve the solution, an ad hoc procedure reduces the number of vehicles and a second objective function drives the search out of local optima. No parameter tuning is required and no random choice is...

Abstract Recent works using deep learning to solve routing problems such as the traveling salesman problem (TSP) have focused on construction heuristics. Such approaches find good quality solutions but require additional procedures beam search and sampling improve achieve state-of-the-art performance. However, few studies improvement heuristics, where a given solution is improved until reaching...

The purpose of this paper is to propose effective parallelization strategies for Local Search algorithms on Graphics Processing Units (GPU). We consider the distribution of the 3-opt neighborhood structure embedded in the Iterated Local Search framework. Three resulting approaches are evaluated and compared on both speedup and solution quality on a state-of-the-art Fermi GPU architecture. Solvi...