Fuzzy logic-controlled diversity-based multi-objective memetic algorithm applied to a frequency assignment problem

Segredo, Eduardo; Segura, Carlos; León, Coromoto

doi:10.1016/j.engappai.2014.01.005

Cited by 12 publications

(15 citation statements)

References 32 publications

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“…A formal definition of this meta-heuristic can be found in [11]. This meta-heuristic was first proposed in [12] and was selected because it yielded the best solutions considering different instances of the FAP in previous work [3], [11], [12].…”

Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

“…Note that here, as is common in other work, we only consider one additional objective. We previously tested two potential candidate metrics [3], [11]: the Distance to the Closest Neighbour (DCN) and the Average Distance to all Individuals (ADI), proposed in [14] and [15], respectively. Both of them should be maximised.…”

Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

“…One of these parameters (p m ) is continuous and the other one (R) is discrete. These parameters were independently adapted by the use of FLCs and HHs as control methods, and as a result, two separate studies were carried out [3]. In both studies, FLCs and HHs demonstrated high performance when controlling the parameters of the NM operator in terms of the quality of frequency plans were obtained.…”

Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

“…In [3], Fuzzy Logic Controllers (FLCs) and Hyper-heuristics (HHs) were compared as control methods for adapting two numeric parameters of a diversity-based multi-objective Memetic Algorithm (MA) applied to a Frequency Assignment Problem (FAP). The main drawback was that both parameters were optimised independently, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid parameter control approach applied to a diversity-based multi-objective memetic algorithm for frequency assignment problems

Segredo

Paechter

Hart

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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In order to address the difficult issue of parameter setting within a diversity-based Multi-objective Evolutionary Algorithm (MOEA), we recently proposed a hybrid control scheme based on both Fuzzy Logic Controllers (FLCs) and Hyperheuristics (HHs). The method simultaneously adapts both symbolic and numeric parameters and was shown to be effective when controlling a diversity-based MOEA applied to a range of benchmark problems. Here, we show that the hybrid control scheme generalises to other meta-heuristics by using it to adapt several parameters of a diversity-based multi-objective Memetic Algorithm (MA) applied to a Frequency Assignment Problem (FAP). Using real-world instances of the FAP, we demonstrate that our proposed parameter control method outperforms parameter tuning of the MA. The results provide new evidence that the method can be successfully applied to significantly more complex problems than the benchmarks previously tested.

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Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

Section: Diversity-based Multi-objective Memetic Algorithm For Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid parameter control approach applied to a diversity-based multi-objective memetic algorithm for frequency assignment problems

Segredo

Paechter

Hart

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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“…The said variant was proposed and has been successfully applied by the authors in previous work [16,17,20]. It is based on using a scoring and a selection strategy for choosing the most suitable low-level configuration.…”

Section: Hyper-heuristicmentioning

confidence: 99%

Hybridisation of Evolutionary Algorithms Through Hyper-heuristics for Global Continuous Optimisation

Segredo

Lalla-Ruiz

Hart

et al. 2016

Lecture Notes in Computer Science

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Abstract. Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the improvement that might be made by applying each algorithm. We use a differential evolution algorithm and a genetic algorithm as the two metaheuristics and assess performance on a suite of 18 functions provided by the Generalization-based Contest in Global Optimization (genopt). The experimental evaluation shows that the hybridisation is able to provide an improvement with respect to the results obtained by both the differential evolution scheme and the genetic algorithm when they are executed independently. In addition, the high performance of our hybrid approach allowed two out of the three prizes available at genopt to be obtained.

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Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

Moscato

Mathieson

2019

Business and Consumer Analytics: New Ideas

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Fuzzy logic-controlled diversity-based multi-objective memetic algorithm applied to a frequency assignment problem

Cited by 12 publications

References 32 publications

Hybrid parameter control approach applied to a diversity-based multi-objective memetic algorithm for frequency assignment problems

Hybrid parameter control approach applied to a diversity-based multi-objective memetic algorithm for frequency assignment problems

Hybridisation of Evolutionary Algorithms Through Hyper-heuristics for Global Continuous Optimisation

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

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