# A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks

@article{Fontes2013AMH, title={A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks}, author={Dalila B.M.M. Fontes and Jos{\'e} Fernando Gonçalves}, journal={Optimization Letters}, year={2013}, volume={7}, pages={1303-1324} }

Genetic algorithms and other evolutionary algorithms have been successfully applied to solve constrained minimum spanning tree problems in a variety of communication network design problems. In this paper, we enlarge the application of these types of algorithms by presenting a multi-population hybrid genetic algorithm to another communication design problem. This new problem is modeled through a hop-constrained minimum spanning tree also exhibiting the characteristic of flows. All nodes, except…

## 24 Citations

### Hop-constrained Minimum Spanning Tree Problems in Nonlinear Cost Network Flows : an Ant Colony Optimization Approach

- Computer Science
- 2013

This work proposes a hybrid heuristic, based on Ant Colony Optimization and on Local Search, to solve this class of problems given its combinatorial nature and also that the total costs are nonlinearly flow dependent with a fixed-charge component.

### The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach

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- 2015

This work proposes a hybrid heuristic, based on ant colony optimization and on local search, to solve the Hop-Constrained Minimum cost Flow Spanning Tree problem with nonlinear costs, and proves to be able to find an optimum solution in more than 75 % of the runs.

### Solving Hop-constrained MST problems with ACO

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### A hybrid biased random key genetic algorithm approach for the unit commitment problem

- Computer ScienceJ. Comb. Optim.
- 2014

The algorithm developed is a hybrid biased random key genetic algorithm (HBRKGA) that uses random keys to encode the solutions and introduces bias both in the parent selection procedure and in the crossover strategy.

### A biased random-key genetic algorithm for the tree of hubs location problem

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This paper proposes a biased random key genetic algorithm for solving the tree of hubs location problem, in which hubs are connected by means of a tree and the overall network infrastructure relies on a spanning tree.

### Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions

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### Survey on applications of biased-random key genetic algorithms for solving optimization problems

- Computer Science2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
- 2015

From the survey, a number of findings include: number of studies tends to increase over the last five years dealing with various combinatorial optimization problems, however limited research deals with continuous variables, and local search procedure is the typical hybridization method used to enhance the performance of the algorithms.

### A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria

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This work proposes to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it and shows that the method proposed compares favourably against alternative approaches in most cases analysed.

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The algorithm proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy, which has shown the proposed methodology to be an effective and effective combinatorial optimization method.

### Random-Key Genetic Algorithms

- Computer Science, MathematicsHandbook of Heuristics
- 2018

This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-keys genetic algorithms, an evolutionary metaheuristic for discrete and global optimization.

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