Application of population evolvability in a hyper-heuristic for dynamic multi-objective optimization

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DP...

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Vydané v:Technological and economic development of economy Ročník 25; číslo 5; s. 951 - 978
Hlavní autori: Macias-Escobar, Teodoro, Cruz-Reyes, Laura, Dorronsoro, Bernabé, Fraire-Huacuja, Héctor, Rangel-Valdez, Nelson, Gómez-Santillán, Claudia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Vilnius Vilnius Gediminas Technical University 12.07.2019
Predmet:
Change detection
DNSGA-II
dynamic multi-objective evolutionary algorithm
dynamic multi-objective optimization
Evolutionary algorithms
fitness landscape analysis
Heuristic
Heuristic methods
hyper-heuristic
Multiple objective analysis
Optimization
Population
population evolvability
fitness landscape analysis
dynamic multi-objective evolutionary algorithm
DNSGA-II
hyper-heuristic
dynamic multi-objective optimization
population evolvability
ISSN:2029-4913, 2029-4921
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Shrnutí:It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems. First published online 12 July 2019
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2029-4913
2029-4921
DOI:10.3846/tede.2019.10291