TY - GEN
T1 - The Hybrid Framework for Multi-objective Evolutionary Optimization Based on Harmony Search Algorithm
AU - Doush, Iyad Abu
AU - Bataineh, Mohammad Qasem
AU - El-Abd, Mohammed
N1 - Publisher Copyright:
© 2019, Springer International Publishing AG, part of Springer Nature.
PY - 2019
Y1 - 2019
N2 - In evolutionary multi-objective optimization, an evolutionary algorithm is invoked to solve an optimization problem involving concurrent optimization of multiple objective functions. Many techniques have been proposed in the literature to solve multi-objective optimization problems including NSGA-II, MOEA/D and MOPSO algorithms. Harmony Search (HS), which is a relatively new heuristic algorithm, has been successfully used in solving multi-objective problems when combined with non-dominated sorting (NSHS) or the breakdown of the multi-objectives into scalar sub-problems (MOHS/D). In this paper, the performance of NSHS and MOHS/D is enhanced by using a previously proposed hybrid framework. In this framework, the diversity of the population is measured every a predetermined number of iterations. Based on the measured diversity, either local search or a diversity enhancement mechanism is invoked. The efficiency of the hybrid framework when adopting HS is investigated using the ZDT, DTLZ and CEC2009 benchmarks. Experimental results confirm the improved performance of the hybrid framework when incorporating HS as the main algorithm.
AB - In evolutionary multi-objective optimization, an evolutionary algorithm is invoked to solve an optimization problem involving concurrent optimization of multiple objective functions. Many techniques have been proposed in the literature to solve multi-objective optimization problems including NSGA-II, MOEA/D and MOPSO algorithms. Harmony Search (HS), which is a relatively new heuristic algorithm, has been successfully used in solving multi-objective problems when combined with non-dominated sorting (NSHS) or the breakdown of the multi-objectives into scalar sub-problems (MOHS/D). In this paper, the performance of NSHS and MOHS/D is enhanced by using a previously proposed hybrid framework. In this framework, the diversity of the population is measured every a predetermined number of iterations. Based on the measured diversity, either local search or a diversity enhancement mechanism is invoked. The efficiency of the hybrid framework when adopting HS is investigated using the ZDT, DTLZ and CEC2009 benchmarks. Experimental results confirm the improved performance of the hybrid framework when incorporating HS as the main algorithm.
KW - Harmony Search
KW - Multi-objective optimization
KW - Multi-objective optimization evolutionary algorithms
UR - http://www.scopus.com/inward/record.url?scp=85047354908&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91337-7_13
DO - 10.1007/978-3-319-91337-7_13
M3 - Conference contribution
SN - 9783319913360
T3 - Advances in Intelligent Systems and Computing
SP - 134
EP - 142
BT - Lecture Notes in Real-Time Intelligent Systems
A2 - Mohamed, Lahby
A2 - Pichappan, Pit
A2 - Mizera-Pietraszko, Jolanta
PB - Springer Verlag
T2 - 2nd International Conference on Real-Time Intelligent Systems, RTIS 2017
Y2 - 18 October 2017 through 20 October 2017
ER -