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Solving Continuous Optimization Problems using a Dynamic Self-Adaptive Harmony Search Algorithm
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Ali Kattan1,Rosni Abdullah2
*1 School of Computer Sciences, USM, 11800 Penang, Malaysia, Email: kattan@ieee.org
2 School of Computer Sciences, USM, 11800 Penang, Malaysia, rosni@cs.usm.my
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Abstract
.In order to solve global optimization problems of continuous functions, researchers rely on using meta-heuristic algorithms to overcome the computational drawbacks of the existing numerical methods. A recent such meta-heuristic is the Harmony Search algorithm that was inspired from the music improvisation process and has been applied successfully for solving such problems. The proper settings of the algorithm parameters prior to starting the optimization process plays a crucial role in its overall performance and ability to converge to a good solution. Several improvements have been suggested to automatically tune some of these optimization parameters to achieve better results in comparison to the original. This paper proposes new dynamic and self-adaptive Harmony Search algorithm that utilizes two new quality measures to drive the optimization process dynamically. They key difference between the proposed algorithm and many recent improvements is that the values for the pitch-adjustment rate and the bandwidth optimization parameters are determined independently from the current improvisation count and hence do not change monotonically but dynamically. Results show the superiority of the proposed method against several recent methods using some common benchmarking problems.
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Keywords
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computational intelligence; meta- heuristic
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