TY - GEN
T1 - On different stopping criteria for multi-objective harmony search algorithms
AU - Doush, Iyad Abu
AU - Bataineh, Mohammad Qasem
AU - El-Abd, Mohammed
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery. All rights reserved.
PY - 2019/3/23
Y1 - 2019/3/23
N2 - In evolutionary multi-objective optimization, an evolutionary algorithm is used to solve an optimization problem having multiple, and usually conflicting objective functions. Previous proposed approaches to solve multi-objective optimization problems include NSGA-II, MOEA/D, MOPSO, and MOHS/D algorithms. In our previous work, we enhanced the performance of MOHS/D using a hybrid framework with population diversity monitoring. The population diversity was measured every a predetermined number of iterations to either invoke local search or a diversity enhancement mechanism. In this work, two different stopping criteria are compared using four the HS hybrid frameworks we previously proposed. The stopping criteria compared are the moving average and MGBM. The experimental study is carried using the ZDT, DTLZ and CEC2009 benchmarks. The experimental results show that the moving average stopping criteria gives better results for the majority of the datasets.
AB - In evolutionary multi-objective optimization, an evolutionary algorithm is used to solve an optimization problem having multiple, and usually conflicting objective functions. Previous proposed approaches to solve multi-objective optimization problems include NSGA-II, MOEA/D, MOPSO, and MOHS/D algorithms. In our previous work, we enhanced the performance of MOHS/D using a hybrid framework with population diversity monitoring. The population diversity was measured every a predetermined number of iterations to either invoke local search or a diversity enhancement mechanism. In this work, two different stopping criteria are compared using four the HS hybrid frameworks we previously proposed. The stopping criteria compared are the moving average and MGBM. The experimental study is carried using the ZDT, DTLZ and CEC2009 benchmarks. The experimental results show that the moving average stopping criteria gives better results for the majority of the datasets.
KW - Continuous optimization
KW - Harmony search
KW - Hybrid framework
KW - Multi-objective optimization
KW - Stopping criteria
UR - http://www.scopus.com/inward/record.url?scp=85068745989&partnerID=8YFLogxK
U2 - 10.1145/3325773.3325774
DO - 10.1145/3325773.3325774
M3 - Conference contribution
AN - SCOPUS:85068745989
T3 - ACM International Conference Proceeding Series
SP - 30
EP - 34
BT - Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2019
PB - Association for Computing Machinery
T2 - 3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2019
Y2 - 23 March 2019 through 24 March 2019
ER -