A Study of Recombination Operators for the Cyclic Bandwidth Problem

Ren, Jintong; Hao, Jin‐Kao; Rodríguez-Tello, Eduardo

doi:10.1007/978-3-030-45715-0_14

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…To assess its usefulness, a new variant (HGA5) is constructed by disabling the mutation operator in HGA. HGA is then compared with HGA5 in terms of population diversity by using the following diversity measure [28]. Let

| 𝒫 |

be the number of solutions in the population

𝒫

.…”

Section: Additional Experimentsmentioning

confidence: 99%

A hybrid genetic algorithm for the Hamiltonian p‐median problem

He,

Hao,

2023

Networks

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The Hamiltonian p‐median problem consists of finding p( is given) non‐intersecting Hamiltonian cycles in a complete edge‐weighted graph such that each cycle visits at least three vertices and each vertex belongs to exactly one cycle, while minimizing the total cost of pcycles. In this work, we present an effective and scalable hybrid genetic algorithm to solve this computationally challenging problem. The algorithm combines an edge‐assembly crossover to generate promising offspring solutions from high‐quality parents, and a multiple neighborhood local search to improve each offspring solution. To promote population diversity, the algorithm applies a mutation operator to the offspring solutions and a quality‐and‐distance update strategy to manage the population. We compare the method to the best reference algorithms in the literature based on three sets of 145 popular benchmark instances (with up to 318 vertices), and report improved best upper bounds for eight instances. To evaluate the scalability of the method, we perform experiments on a new set of 70 large instances (with up to 1060 vertices). We examine the contributions of key components of the algorithm.

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