Enhanced Multi-Objective Grey Wolf Optimization using Adaptive Diversity Tuning and Levy Flights

This study proposes an enhanced Multi-Objective Grey Wolf Optimizer (MOGWO) using adaptive population diversity tuning and levy flight theories (EMOGWO-ADTLF). It addresses the issues of parameter tunning by balancing exploration and exploitation. Using MATLAB and Python library Pymoo, the study imp...

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Veröffentlicht in:Carpathian journal of electrical engineering Jg. 18; H. 1; S. 7 - 35
Hauptverfasser: Asigri, Peter, Frimpong, Emmanuel Asuming, Anto, Emmanuel Kwaku, Kwegyir, Daniel, Effah, Francis Boafo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Baia Mare North University Centre of Baia Mare 01.12.2024
Technical University of Cluj-Napoca
Schlagworte:
adaptive population diversity
Algorithms
Archives & records
Decision making
Engineering
Exploitation
grey wolf optimizer
Leadership
levy flight
multi-objective optimization
Multiple objective analysis
Objectives
Optimization
Performance evaluation
Tuning
ISSN:1843-7583, 1843-7583
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Abstract This study proposes an enhanced Multi-Objective Grey Wolf Optimizer (MOGWO) using adaptive population diversity tuning and levy flight theories (EMOGWO-ADTLF). It addresses the issues of parameter tunning by balancing exploration and exploitation. Using MATLAB and Python library Pymoo, the study implemented and evaluated the performance of EMOGWO-ADTLF using multi-objective test problems. The results were compared to high-performing algorithms like MOGWO, Non-Dominated Sorting Grey Wolf Optimizer (NSGWO), Dynamic Chaos MOGWO (DCMOGWO), Multi-Objective Mayfly Algorithm (MMA), Multi-Objective Antlion Algorithm (MOALO) and Multi-Objective Dragonfly Algorithm (MODA). In this work, inverted generational distance (IGD) and hypervolume (HV) were the metrics used to measure the performance of algorithms. The metrics measure the diversity, coverage, and spread of solutions. The results obtained showed the potency of EMOGWO-ADTLF in approximating the Pareto fronts. It ranks first in overall average scores in IGD and HV, with total rank scores of 17 and 18, respectively.
AbstractList This study proposes an enhanced Multi-Objective Grey Wolf Optimizer (MOGWO) using adaptive population diversity tuning and levy flight theories (EMOGWO-ADTLF). It addresses the issues of parameter tunning by balancing exploration and exploitation. Using MATLAB and Python library Pymoo, the study implemented and evaluated the performance of EMOGWO-ADTLF using multi-objective test problems. The results were compared to high-performing algorithms like MOGWO, Non-Dominated Sorting Grey Wolf Optimizer (NSGWO), Dynamic Chaos MOGWO (DCMOGWO), Multi-Objective Mayfly Algorithm (MMA), Multi-Objective Antlion Algorithm (MOALO) and Multi-Objective Dragonfly Algorithm (MODA). In this work, inverted generational distance (IGD) and hypervolume (HV) were the metrics used to measure the performance of algorithms. The metrics measure the diversity, coverage, and spread of solutions. The results obtained showed the potency of EMOGWO-ADTLF in approximating the Pareto fronts. It ranks first in overall average scores in IGD and HV, with total rank scores of 17 and 18, respectively.
Author Kwegyir, Daniel
Asigri, Peter
Frimpong, Emmanuel Asuming
Anto, Emmanuel Kwaku
Effah, Francis Boafo
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  surname: Asigri
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  givenname: Emmanuel Asuming
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  fullname: Frimpong, Emmanuel Asuming
  organization: Kwame Nkrumah University of Science and Technology Kumasi, Ghana
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  organization: Kwame Nkrumah University of Science and Technology Kumasi, Ghana
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  organization: Kwame Nkrumah University of Science and Technology Kumasi, Ghana
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Technical University of Cluj-Napoca
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SubjectTerms adaptive population diversity
Algorithms
Archives & records
Decision making
Engineering
Exploitation
grey wolf optimizer
Leadership
levy flight
multi-objective optimization
Multiple objective analysis
Objectives
Optimization
Performance evaluation
Tuning
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