A biased random-key genetic algorithm for the maximum quasi-clique problem

Pinto, Bruno; Ribeiro, Celso C.; Rosseti, Isabel; Plastino, Alexandre

doi:10.1016/j.ejor.2018.05.071

Cited by 39 publications

(27 citation statements)

References 18 publications

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“…Biased‐randomization techniques make use of Monte Carlo simulation to enhance the performance of constructive heuristics (Faulin and Juan, ; Faulin et al., ). These techniques have been successfully employed to deal with different optimization problems, including vehicle routing problems (Fikar et al., ; Belloso et al., ), scheduling problems (Juan et al., ; Brandão et al., , ), facility‐location problems (Quintero‐Araujo et al., , ), open stacks problems (Gonçalves et al., ), and quasi‐clique problems (Pinto et al., ), among many others.…”

Section: Our Br‐ils Solving Approachmentioning

confidence: 99%

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Ferone

Hatami

González-Neira

et al. 2019

Int Trans Operational Res

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Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow‐shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow‐shop configuration; in the second stage, the outcomes of each flow‐shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter‐less algorithm that makes use of a biased‐randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state‐of‐the‐art approaches, obtaining several new best solutions.

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Section: Our Br‐ils Solving Approachmentioning

confidence: 99%

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Ferone

Hatami

González-Neira

et al. 2019

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…We also compare the performance of the proposed algorithms with that of the basic GA and PSO [65], [66]. The GA is chosen for this comparison because the proposed RKBMO and DBMO are evolutionary based and the GA is the standard algorithm for evolutionary algorithms [67], [68], while PSO has been widely improved and used to address many combinatorial optimization problems [24], [43], [46], [69], [70]. The GA has been implemented using roulette wheel selection with order crossover [71].…”

Section: Computational Resultsmentioning

confidence: 99%

Bird Mating Optimizer for Combinatorial Optimization Problems

et al. 2020

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The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance. However, the algorithm has not yet been applied for solving combinatorial optimization problems. Thus, the formulation may not be able to generate a discrete feasible solution. Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem. However, there is no evidence which methodology is better for solving combinatorial optimization problems. Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems. In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods. The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization. Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances. Indeed, it is able to attain the best-known results in some of the BAP benchmark instances. These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems. INDEX TERMS Heuristics, bird mating optimizer, berth allocation problem, travelling salesman problem, random-key, combinatorial optimization, metaheuristics.

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“…As the original source-code of CS* was unavailable and the experiments reported in Chaves et al [9] have been made on a different processor, we followed the idea of Pinto et al [20] by considering an approximate scale ratio to compare the speed of both processors. Concerning the single-rating performance, once the algorithms were tested on single-thread environments, the respective ratio for the two CPU models is 463/2184 ≈ 0.21, according to the PassMark benchmark [1].…”

Section: Mathematical Formulationsmentioning

confidence: 99%

A Branch-and-Cut and MIP-based heuristics for the Prize-Collecting Travelling Salesman Problem

2021

Self Cite

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The Prize Collecting Traveling Salesman Problem (PCTSP) represents a generalization of the well-known Traveling Salesman Problem. The PCTSP can be associated with a salesman that collects a prize in each visited city and pays a penalty for each unvisited city, with travel costs among the cities. The objective is to minimize the sum of the costs of the tour and penalties, while collecting a minimum amount of prize. This paper suggests MIP-based heuristics and a branch-and-cut algorithm to solve the PCTSP. Experiments were conducted with instances of the literature, and the results of our methods turned out to be quite satisfactory.

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A biased random-key genetic algorithm for the maximum quasi-clique problem

Cited by 39 publications

References 18 publications

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Bird Mating Optimizer for Combinatorial Optimization Problems

A Branch-and-Cut and MIP-based heuristics for the Prize-Collecting Travelling Salesman Problem

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