Simulated Annealing for Finding TSP Lower Bound

2017 ◽  
pp. 45-55
Author(s):  
Łukasz Strąk ◽  
Wojciech Wieczorek ◽  
Arkadiusz Nowakowski
Keyword(s):  
Simulated Annealing ◽  
Lower Bound

scholarly journals The performance of the quantum adiabatic algorithm on spike Hamiltonians

2017 ◽  
Vol 15 (02) ◽  
pp. 1750011 ◽  
Author(s):  
Linghang Kong ◽  
Elizabeth Crosson
Keyword(s):  
Simulated Annealing ◽  
Lower Bound ◽  
Ground State ◽  
Recursion Relation ◽  
Spectral Gap ◽  
Hamming Weight ◽  
Wkb Method ◽  
Adiabatic Evolution ◽  
Comparison Argument ◽  
Avoided Crossings

Spike Hamiltonians arise from optimization instances for which the adiabatic algorithm provably out performs classical simulated annealing. In this work, we study the efficiency of the adiabatic algorithm for solving the “the Hamming weight with a spike” problem by analyzing the scaling of the spectral gap at the critical point for various sizes of the barrier. Our main result is a rigorous lower bound on the minimum spectral gap for the adiabatic evolution when the bit-symmetric cost function has a thin but polynomially high barrier, which is based on a comparison argument and an improved variational ansatz for the ground state. We also adapt the discrete WKB method for the case of abruptly changing potentials and compare it with the predictions of the spin coherent instanton method which was previously used by Farhi, Goldstone and Gutmann. Finally, our improved ansatz for the ground state leads to a method for predicting the location of avoided crossings in the excited energy states of the thin spike Hamiltonian, and we use a recursion relation to understand the ordering of some of these avoided crossings as a step towards analyzing the previously observed diabatic cascade phenomenon.

scholarly journals MINIMIZING THE MAKESPAN FOR UNRELATED PARALLEL MACHINES

2007 ◽  
Vol 16 (03) ◽  
pp. 399-415 ◽  
Author(s):  
YUNSONG GUO ◽  
ANDREW LIM ◽  
BRIAN RODRIGUES ◽  
LIANG YANG
Keyword(s):  
Simulated Annealing ◽  
Lower Bound ◽  
Tabu Search ◽  
Parallel Machines ◽  
Parallel Machine ◽  
Experimental Results ◽  
Neighborhood Search ◽  
Unrelated Parallel Machines ◽  
Parallel Machine Problem ◽  
Machine Problem

In this paper, we study the unrelated parallel machine problem for minimizing the makespan, which is NP-hard. We used Simulated Annealing (SA) and Tabu Search (TS) with Neighborhood Search (NS) based on the structure of the problem. We also used a modified SA algorithm, which gives better results than the traditional SA and developed an effective heuristic for the problem: Squeaky Wheel Optimization (SWO) hybrid with TS. Experimental results average 2.52% from the lower bound and are within acceptable timescales improving current best results for the problem.

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Chairul Rahmadsyah Manik
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Production Scheduling ◽  
Production Control ◽  
Delivery Time ◽  
Production Schedule ◽  
A Company ◽  
Predetermined Time ◽  
Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan.   Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms
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