A genetic algorithm approach to large scale combinatorial optimization problems in the advertising industry

2002 ◽  
Author(s):  
K. Ohkura ◽  
T. Igarashi ◽  
K. Ueda ◽  
S. Okauchi ◽  
H. Matsunaga
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Advertising Industry ◽  
Combinatorial Optimization Problems ◽  
Genetic Algorithm Approach

A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window

2017 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Lahcene Guezouli ◽  
Samir Abdelhamid
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Vehicle Routing Problem ◽  
Processing Time ◽  
Optimization Problems ◽  
Time Window ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Capacitated Vehicle

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

scholarly journals Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems

2019 ◽  
Vol 5 (4) ◽  
pp. eaav2372 ◽  
Author(s):  
Hayato Goto ◽  
Kosuke Tatsumura ◽  
Alexander R. Dixon
Keyword(s):  
Combinatorial Optimization ◽  
Hamiltonian Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximate Solutions ◽  
Combinatorial Optimization Problems ◽  
Bifurcation Phenomena ◽  
Field Programmable ◽  
Max Cut Problem ◽  
Adiabatic Optimization

Combinatorial optimization problems are ubiquitous but difficult to solve. Hardware devices for these problems have recently been developed by various approaches, including quantum computers. Inspired by recently proposed quantum adiabatic optimization using a nonlinear oscillator network, we propose a new optimization algorithm simulating adiabatic evolutions of classical nonlinear Hamiltonian systems exhibiting bifurcation phenomena, which we call simulated bifurcation (SB). SB is based on adiabatic and chaotic (ergodic) evolutions of nonlinear Hamiltonian systems. SB is also suitable for parallel computing because of its simultaneous updating. Implementing SB with a field-programmable gate array, we demonstrate that the SB machine can obtain good approximate solutions of an all-to-all connected 2000-node MAX-CUT problem in 0.5 ms, which is about 10 times faster than a state-of-the-art laser-based machine called a coherent Ising machine. SB will accelerate large-scale combinatorial optimization harnessing digital computer technologies and also offer a new application of computational and mathematical physics.

scholarly journals HIERARCHICAL CLUSTERING APPROACH WITH HYBRID GENETIC ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS

2016 ◽  
pp. 214-218
Author(s):  
M. H. MEHTA ◽  
V. V. KAPADIA
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Hierarchical Clustering ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Combinatorial Optimization Problems ◽  
Input Size ◽  
Clustering Approach ◽  
Definite Interval

Engineering field has inherently many combinatorial optimization problems which are hard to solve in some definite interval of time especially when input size is big. Although traditional algorithms yield most optimal answers, they need large amount of time to solve the problems. A new branch of algorithms known as evolutionary algorithms solve these problems in less time. Such algorithms have landed themselves for solving combinatorial optimization problems independently, but alone they have not proved efficient. However, these algorithms can be joined with each other and new hybrid algorithms can be designed and further analyzed. In this paper, hierarchical clustering technique is merged with IAMB-GA with Catfish-PSO algorithm, which is a hybrid genetic algorithm. Clustering is done for reducing problem into sub problems and effectively solving it. Results taken with different cluster sizes and compared with hybrid algorithm clearly show that hierarchical clustering with hybrid GA is more effective in obtaining optimal answers than hybrid GA alone.

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