Solving the Set Covering Problem with a Shuffled Frog Leaping Algorithm

Crawford, Broderick; Soto, Ricardo; Peña, Cristian; Palma, Wenceslao; Johnson, Franklin; Paredes, Fernando

doi:10.1007/978-3-319-15705-4_5

Cited by 18 publications

(8 citation statements)

References 26 publications

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“…Each instance was pre-processed. In addition to being compared with different literature optimization techniques, such as Cat Swarm Optimization (BCSO) [ 7 ], Binary Firefly Optimization (BFO) [ 12 ], Binary Shuffled Frog Leaping Algorithm (BSFLA) [ 11 ], Binary Artificial Bee Colony (BABC) [ 9 ] and Binary Electromagnetism-Like Algorithm (BELA) [ 33 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Ambidextrous Socio-Cultural Algorithms

Lemus-Romani

Crawford

Soto

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

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Section: Experiments and Resultsmentioning

confidence: 99%

Ambidextrous Socio-Cultural Algorithms

Lemus-Romani

Crawford

Soto

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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“…We compared our binary learning and teaching based optimization algorithm (bTLBO) with the global optimal (Zopt) in [27], as well as with recent work solving SCP based on bio-inspired metaheuristics. The metaheuristics are: Binary Cat Swarm Optimization (BCSO) [28], Binary Firefly Optimization (BFO) [22], Binary Shuffled Frog Leaping Algorithm (BSFLA) [29], Binary Electromagnetism -Like Algorithm (BELA) [30], and Binary Artificial Bee Colony (BABC) [31]. The results are summarized in Tab.…”

Section: Resultsmentioning

confidence: 99%

A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem

et al. 2020

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The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

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“…For instance, ant colony optimization [32][33][34][35], simulated annealing [36], tabu search [37], and genetic algorithms [38] have extensively been proposed to tackle the classic set covering problem. In recent years, research has been driven towards solving this problem by using recent bioinspired algorithms, such as teaching-learning based optimization [39] firefly optimization [40], cat swarm optimization [41], shuffled frog leaping [42], artificial bee colony [43], cuckoo search [44], and black hole algorithm [44], among others.…”

Section: Related Workmentioning

confidence: 99%

“…To evidence the performance of our adaptive approach, we perform a comparison with different approximation techniques: binary cat swarm optimization (BCSO) [41], binary firefly optimization (BFO) [40], binary shuffled frog leaping algorithm (BSFLA) [42], binary artificial bee colony (BABC) [43], and binary electromagnetism-like algorithm (BELA) [66].…”

Section: Bh-as V/s Other Optimization Techniquesmentioning

confidence: 99%

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Soto

Crawford

Olivares

et al. 2018

Mathematical Problems in Engineering

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Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics.

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Solving the Set Covering Problem with a Shuffled Frog Leaping Algorithm

Cited by 18 publications

References 26 publications

Ambidextrous Socio-Cultural Algorithms

Ambidextrous Socio-Cultural Algorithms

A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

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