Constrained Nonlinear Optimization in Business

2014 ◽  
pp. 523-535
Author(s):  
William P. Fox
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Nonlinear Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Transportation Networks ◽  
Business Environment ◽  
Traveling Salesman ◽  
Constrained Nonlinear Optimization ◽  
The Traveling Salesman Problem

We present both classical analytical, numerical, and heuristic techniques to solve constrained optimization problems relating to business, industry, and government. We briefly discuss other methods such as genetic algorithm. Today's business environment has many resource challenges to their attempts to maximize profits or minimize costs for which constrained optimization might be used. Facility location and transportation networks techniques are often used as well as the traveling salesman problem.

scholarly journals Orthogonal Genetic Algorithm and its Application in Traveling Salesman Problem

2012 ◽  
Vol 6-7 ◽  
pp. 290-293
Author(s):  
Han Min Liu ◽  
Qing Hua Wu ◽  
Xue Song Yan
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Local Minimum ◽  
Optimization Problems ◽  
Orthogonal Test ◽  
Traveling Salesman ◽  
Crossover Operator ◽  
Great Efficiency ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most widely studied NP-hard combinatorial optimization problems. Its statement is deceptively simple, and yet it remains one of the most challenging problems and traditional genetic algorithm trapped into the local minimum easily for solving this problem. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive orthogonal local search to prevent local convergence to form a new orthogonal genetic algorithm. The new algorithm shows great efficiency in solving TSP with the problem scale under 300 under the experiment results analyze.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

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